# State of the art applications of deep learning within tracking and detecting
marine debris: A survey.

Zoe Moorton11footnotemark: 1 Dr Zeyneb Kurt22footnotemark: 2 Dr Wai Lok
Woo33footnotemark: 3 Department of Computer and Information Sciences,
University of Northumbria, Newcastle Upon Tyne, UK<EMAIL_ADDRESS>
###### Abstract

Deep learning techniques have been explored within the marine litter problem
for approximately 20 years but the majority of the research has developed
rapidly in the last five years. We provide an in-depth, up to date, summary
and analysis of 28 of the most recent and significant contributions of deep
learning in marine debris.
From cross referencing the research paper results, the YOLO family
significantly outperforms all other methods of object detection but there are
many respected contributions to this field that have categorically agreed that
a comprehensive database of underwater debris is not currently available for
machine learning. Using a small dataset curated and labelled by us, we tested
YOLOv5 on a binary classification task and found the accuracy was low and the
rate of false positives was high; highlighting the importance of a
comprehensive database.
We conclude this survey with over 40 future research recommendations and open
challenges.

###### keywords:

marine debris, artificial intelligence, object detection, remote sensing, data
acquisition

††journal: ArXiv

##  1 Introduction

The most represented method of approaching the marine debris problem so far
has been object detection for floating debris and tracking it with remote
sensing.
Within this paper; the authors use various terms such as synthetic debris,
anthropogenic debris litter, rubbish or trash of which are all intended to
have the same definition. Quite simply, these items are disposed of by humans
and have found their way into most water systems. A challenging problem as
Watanabe et al. [2019] explains that marine environments have more complex and
variable backgrounds compared to land.
Although, we mean to specifically address marine debris, this research also
explores some techniques that have been used in other water bodies such as
rivers. It is also worth noting that this survey only covers debris of a macro
size and larger. This means that anything less than 5mm has not been included.

###  1.1 Background

Debris in marine environments has exponentially increased which continues to
have destructive results on marine life, the environment and even human
health. There is an overwhelming plethora of literature that documents the
direct impact of synthetic debris on the marine ecosystem.
Inhabitants of marine settings are frequently found entangled within nets and
other materials; or have been discovered mistaking debris as prey or an object
to play with, consequently ingesting the toxic materials [McAdam, 2017]. In
fact, a quantitative overview of marine fauna ingesting waste by Kühn and van
Franeker [2020] has found that over 700 species are confirmed to eat plastic.
Additionally, a review on ghost gear by Stelfox et al. [2016] confirmed that
up until the time of publication, over 40 species had been recorded entangled
in ghost fishing gear alone. Ghost fishing gear is defined by NOAA [2023a] as
“any discarded, lost, or abandoned, fishing gear in the marine environment.
This gear continues to fish and trap animals, entangle and potentially kill
marine life, smother habitat, and act as a hazard to navigation.”
Unfortunately, due to the consequence of trapped animals either rapidly
sinking or being consumed by predators, we are unable to detect or fairly
estimate the number of entangled animals [Sharma and Chatterjee, 2017], [Allen
et al., 2012], [Laist, 1997], [Quayle, 1992], therefore, the data reported so
far can only be a percentage of the scale.
Often mistaken for prey or consumed within foraging habits [NOAA, 2023b], it
is not difficult to see why so many creatures confuse debris as food; some
studies have even observed that due to their size; microplastics resemble
phytoplankton [Boerger et al., 2010] and due to their texture; plastic bags
are confused for jellyfish by turtles [Bugoni et al., 2001], [Tomás et al.,
2002].
Ingesting marine debris results in lethal consequences [Wright et al., 2013],
[Pawar et al., 2016] as plastic fragments absorbs toxic materials and are
highly contaminated with PCB (biphenyl polychlorinated), polypropylene,
polyethylene, polystyrene, heavy metals and other noxious substances [Mato et
al., 2001]. These substances are leached into the consumer [Talsness et al.,
2009] poisoning the marine fauna, it will cause alternative repercussions
including reproductive disorders, hormone changes, higher disease risk or most
commonly; obstructing the gastrointestinal tract, resulting in starvation and
fatality.[Ryan et al., 1988], [Lee et al., 2001], [Oberdörster and Cheek,
2001], [Derraik, 2002], [Rahman and Brazel, 2004], [Plot and Georges, 2011].
Ultimately, plastic debris ends up sinking to the seafloor, where it may stay
for hundreds of years [Goldberg, 1997]. This is impacting organisms underneath
and around it, as it suffocates and blocks the light of the coral and soft
sediment, which is vital to the ocean ecosystem [Chiappone et al., 2005].
Manual efforts have been made to clean up coastlines; Scuba divers such as
those who are a part of the Professional Association of Diving Instructors
(PADI) Dive Against Debris program, [PADI, ] have reportedly collected
307,000kg of debris from dives, since 2011. This means that an impressive
2,132 kg of debris is collected a month. Unfortunately, this is problematic as
scuba diving is an expensive and hazardous method to rely on, debris
collection sometimes requires special training but most notably manual labour
is too slow against the rapid influx of debris - the NOAA [2022] have
estimated that approximately 666,666 tonnes of plastic alone enters the oceans
every month (approximately 8 million metric tonnes a year). This solidifies
the reason why automation and artificial intelligence are our best chances of
efficiently removing synthetic debris in water bodies, worldwide.

###  1.2 The Rise of Plastic

For almost a century, we have become aware of the scope of repercussions
plastic debris is causing and have still not successfully targeted this
dilemma.

In the 1930s, a study Moore et al. [2001] found that a small number of
northern fur seals had been discovered with “various objects caught around
their necks, shoulders and … flippers.” Within the 60s - 70s biologists
studying seabird feeding ecology began to notice plastic in their subjects’
diets, as well as entanglement in plastic debris in a wide variety of marine
organisms. Scientists conducting planktonic and benthic surveys in both the
Atlantic and Pacific oceans found unprecedented numbers of plastic particles
among their samples [Coleman and Wehle, 1984].
Approximately forty years ago, Fowler [1987] identified that entanglement
correlated with the rapid increase of fishing efforts in the North Pacific and
Bering sea in the 1960s. This was when plastic materials began to be
substantially favoured for creating trawl netting and packing bands.
In the 1970s, plastic pollution was found in benthic sediments along the
industrialised coast of Great Britain, near Auckland, New Zealand, and in the
Mediterranean Sea as enormous floating masses. Plastic pellets were found in
coastal regions of the United States, Portugal, Colombia, Lebanon, and remote
sites such as the Aleutian and Galapagos Islands. Members of the Marine
Resources Monitoring, Assessment, and Prediction Program (MARMAP) found large
quantities of raw plastics in the open ocean, particularly in the Sargasso Sea
[Coleman and Wehle, 1984].
Later in the 80’s, plastic ingestion was observed in a variety of marine
animals, including seabirds, fish, sea turtles, and whales. Observations that
plastic debris was causing entanglement and death in marine animals, such as
gulls and fur seals began to be discovered and the comprehensive study by Day
and Fairbanks [1980] identified that seabirds eat plastic because they mistake
it for natural prey and then discovered that birds could no longer breed
effectively.

###  1.3 The Plastic Cycle

Barnacles, kelp [Eriksson et al., 2013] and plankton [Moore et al., 2001] were
discovered with nanoplastics within their systems. These animals are at the
very beginning of the food chain. These are the primary food source for filter
feeders who are unfortunately also killed by discarded fishing gear in the
hundred thousands every year [Laist, 1997]. Whales are essential to
phytoplankton populations; these microscopic creatures single handedly consume
approximately “37 billion metric tons of CO, an estimated 40 percent of all CO
produced” - the equivalent to 1.70 trillion trees or four Amazon forests
[Chami et al., 2019], additionally contributing 50% of the world’s oxygen
within our atmosphere.
Due to plastics passing through the food chain and water; McAdam [2017] states
that there is no untouched aquatic setting now, and this has even extended to
salt marshes [Viehman et al., 2011].
With that in mind, it is an epidemic as multiple studies substantiate the
consumption of micro plastics ($<$5mm artificial polymer particles) through
fish and seafood [Daniel et al., 2020] [Danopoulos et al., 2020] [Daniel et
al., 2021] [Lai et al., 2022] [Dong et al., 2023]. With degradation,
nanoplastics ($<$1$\upmu$m in diameter) are formed [Alimi et al., 2018].
Recent research conducted [Leslie et al., 2022] found four types of artificial
polymers (nanoplastics) in participant’s blood samples including; polyethylene
terephthalate (PET), polyethylene (PE), polymers of styrene and poly methyl
methacrylate (PMMA). Researchers are still not confident on the adverse health
implications this could bring to human life [Smith et al., 2018].
In 2015, researchers predicted that by the year 2025, it is estimated that the
ocean will have over 250 million tonnes of plastic [Jambeck et al., 2015]. Due
to the exposure of UV radiation; abrasion and wave action; plastic will break
down into micro and nanoplastics leading to an estimation of approximately
5.25 trillion plastic particles contaminating the sea surface alone [Alimi et
al., 2018]. Due to their size and irregular properties; these plastics will be
difficult to classify and detect.
At the time of this survey, a recent study by [Eriksen et al., 2023] estimated
that within the ocean, at the year of 2019 there were “82–358 trillion plastic
particles (mean = 171 trillion plastic particles, primarily microplastics,
weighing 1.1–4.9 million tonnes (mean = 2.3 million tonnes)”.
As such, the exposure of marine systems to vast (and rising) portions of
plastics, including micro and nanoplastics, calls for the benefit of advanced
artificial intelligence methods for debris identification.

##  2 A Brief History of AI in Marine Debris

Until 2018, AI for underwater environments and marine debris was hardly
researched. This section briefly outlines the use of conventional AI and
machine learning models with marine debris before 2018.

###  2.1 Timeline

In 2002, Foresti and Gentili [2002] used underwater images acquired by an
autonomous underwater vehicle (AUV) to develop a hierarchical neural tree
classifier (HNTC) for object recognition. The HNTC consisted of macro-pixels
and a neural tree that classified each region into different object classes.
The results showed that the classification performances remained unchanged in
the final images, and the method was slightly slower due to the larger number
of pixels to be classified near the borders because of geometrical effects.
Overall, the proposed method showed promising results for object recognition
in underwater images acquired by an AUV.
Later that year, Boulinguez and Quinquis [2002] used acoustic data collected
from a parametric sonar to classify buried objects. The study proposed two
kinds of methods for selecting key parameters to characterise the 3-D target
shape: methods using 2-D projections of 3-D objects and methods using the full
3-D information to compare it with created models. The results showed that the
proposed 3-D pattern recognition using decision fusion improved classification
rates, however the authors suggested further research to collect data that
could help compare methods in a real environment.

Toal et al. [2002] described the development of an intelligent vision system
for an autonomous underwater vehicle (AUV) that carries out underwater filming
and inspection tasks using a combination of two modules: the video marking
system (VMS) and the target tracking system (TTS). The marking system uses
different approaches to identify which fragments of the video footage are
interesting and discards those that only show the water background. The
researchers developed an adaptive vision routine selector that built optimal
feature vectors depending on the environment. The results showed that the VMS
was successful in detecting interesting events in the video footage, and the
adaptive vision routine selector was able to build optimal feature vectors
depending on the environment.
Two years later, Walther et al. [2004] developed an automated system for
detecting and tracking objects in underwater video streams from remotely
operated underwater vehicles (ROVs). The researchers used video data from ROVs
and implemented a selective attention algorithm to pre-select salient targets
for track initiation. The AI used, adopted a saliency-based attention system
and object recognition systems. The results showed that the system was able to
successfully detect and track objects in the underwater video streams.

In 2006, Mitra et al. [2006] used Lidar return data to detect and classify
underwater objects. They compared different AI techniques, including parallel
neural networks using multilayer perceptron (MLP) and Hierarchical Radial
Basis Function (HRBF), Support Vector Machine (SVM) based Information
Extraction (IE), Bayesian and quadratic classifiers, and a single level
artificial neural network (ANN) architecture. The parallel neural network
architecture using MLP was found to have the highest prediction accuracy. The
proposed system achieved an overall accuracy of 98.9%. They concluded that the
parallel neural network architecture along with SVM-based IE worked
efficiently in detecting and distinguishing the required classes from one
another.
In the same year, Cayirci et al. [2006] focused on the detection and
classification of underwater targets using data mining techniques. The data
used was based on the ambient magnetic and acoustic conditions of the vicinity
of the targets. The AI algorithms used were one rule (OneR), Naïve Bayes, and
J48 decision tree model. The conclusion was that J48 had the highest correct
classification rate (92%) and precision. OneR had a 61% rate and Naïve Bayes
had 84%.

Balas et al. [2006] used data from 157 litter surveys carried out over 49
beaches in Wales, UK to develop an artificial intelligence model for
predicting litter categories and grades. They used both a MLP and a fuzzy
system as AI techniques. The neural network sub-model could predict litter
items in seven categories using general litter data available for a specific
Welsh beach, while the fuzzy system sub-model could consider qualitative data
described by language, such as questionnaires distributed to beach users. The
overall predictions of the number of litter items in seven categories from
general litter items at Welsh beaches were considered satisfactory using both
AI techniques. The authors concluded that their AI model could save on field
effort when fast and reliable estimations of litter categories and grades are
required for management studies of beaches.

###  2.2 Deep Learning for Other Marine Applications

Though not related to marine debris, some noteworthy studies have been
considered in this section, which we believe may add methodological insight
for researchers within the field.

In 2004, Beaufort and Dollfus [2004] updated the artificial neural network
(ANN) Syste‘me de Reconnaissance Automatique de Cocco-lithes (SYRACO) for
automatic identification and counting of coccoliths in sediment samples more
accurately. Due to the intricate complexity and human error of counting
samples, they refined the automated process by using digitised images of
sediment samples and trained the ANN using a collection of coccoliths from
different species. The new version of SYRACO was tested on a collection set
and achieved a correct identification rate of 91% with six actions. The
researchers concluded that the updated version is a robust method for
improving the efficiency in the identification and counting of objects in an
uncontrolled environment, namely, coccoliths in sediment samples. A limitation
expressed by the authors affirms that if a class is underrepresented in a
dataset, then the “diffusion of specimens from a more abundant class may cause
a significant statistical bias”, articulating the importance of balanced
classifications.

The study by Hurtos et al. [2013] aimed to develop an automatic detection
algorithm for underwater chain links using a forward-looking sonar. The
authors communicated the lack of available literature in real time with sonar
data. They collected three datasets and had to enhance the data as the
accuracy against the original raw files dropped dramatically. Their method
showed that the accuracy was “reasonably good” despite the resolution of the
data. The conclusion was that the proposed method had high potential to run in
real-time and could be used for underwater chain link detection and in this
case, cleaning. They elucidate their awareness of their methodology assuming
the chain lying approximately on a plane, which they proclaim could be a
limitation, though it could be beneficial to explore sonar data underwater
further.

Gordan et al. [2006] focused on improving the ability to observe object
recognition in colour underwater images as such data can often be difficult to
analyse due to the conditions. To achieve this, the authors used a SVM
classifier. The data was imagery of a hydro-dam wall with the specific
intention to localise circular shaped objects of known dimension (the pressure
equalisation openings.) The proposed architecture involved extracted pixel
features from regions of interest of approximately the object’s size. In the
classification phase, the underwater image was decomposed into partially
overlapping elementary regions of interest and was classified by the SVM based
on a shape descriptor (the circularity) of the patterns. This shape descriptor
was used for their classification as objects of interest or not, through a
simple threshold comparison. The results showed that the proposed architecture
minimised the false acceptance and false rejection rates. A limitation of this
study could be that the researchers selected only five images of which were
augmented into a 255 image database, therefore, it would be interesting to
observe the results of this method on a new dataset.

These methods have the potential capability to be explored and adapted for the
application of detecting debris underwater. For example, methods such as
SYRACO present the opportunity to detect and assess complex biological and
synthetic structures that humans are prone to making errors with. Sonar could
present an interesting opportunity for application of sonar imaging when
detecting basic metallic objects underwater - particularly in deeper depths,
where the lighting conditions make image collection and object detection
difficult. The authors do specify that as sonar frames cannot detect point
features, it would be an unreliable method on optical imagery such as video
and photo. However, we question if simple, well structured objects could still
be worth investigating. Meanwhile, utilising SVM classifiers on marine footage
(particularly deep sea) could assist with problems caused by low image
quality, low lighting and backscattering from refracted and reflected light on
microscopic structures.

##  3 Deep learning techniques in marine debris identification

In order to tackle the ever growing issue of debris accumulating within marine
environments; researchers have taken to applying more-advanced artificial
intelligence in a variety of methods. A popular choice has been through the
use of remote sensing; with data collected either by drones or through
satellites. Remote sensing has had a positive impact on tracking the way
floating debris collects or travels with currents.
Alternatively, some researchers have taken to applying object detection
methods directly on to the surface of the ocean or by applying these methods
underwater. Throughout the next few sections, we have organised the research
papers within their respective categories in chronological order, so that it
is easier for the reader to analyse the data but also to refer back to certain
papers.

###  3.1 Remote Sensing

In Chiloe, Chile, anthropogenic marine debris (AMD) has become a significant
problem. Researchers Acuña-Ruz et al. [2018] tried to address this by using
three different methods of classification when detecting debris on beaches.
SVM, RF and Linear Discriminant Analysis (LDA). By using these three models,
the researchers created a predictive model for the classification of marine
debris within satellite imagery, of which they could compare and select which
models were best for specific tasks. Their use of three models also helps to
reduce the risk of overfitting to help maintain strong performance levels on
new data. SVM performed the strongest for the classification of debris in
satellite imagery with an overall accuracy of 80% when validated against
ground truth data. It successfully detected expanded polystyrene and other
plastic mixtures on the beach with a spatial resolution between 0.3m to 1.2m
and conducted with low errors. The RF and LDA models also performed fairly
well with accuracies of 77% and 70% respectively. Due to weathering processes
the authors found that the spectral signatures of the debris varied widely;
for example Styrofoam shows an increase in reflectance by 30%.

The aim of the study by Jakovljevic et al. [2020] was to develop an efficient
method for mapping floating plastic. Using two sites, their study is split
into two parts; firstly they used UAV images to map where the plastic was but
in their second part, they deployed nets to collect the litter. The authors
successfully used semantic segmentation pixel classification to detect and map
the plastic within water bodies using the ResUNet50 extension. They found that
even in shallow waters the model performed well with an average F1 score of
0.89 across the three categories of plastics they searched for. The authors do
highlight their agreement with the study by Ji et al. [2015], who articulate
the importance of balanced training sets, as within this research, Jakovljevic
et al. [2020] found that mixed pixels would be confused as the majority class.
The authors continue their research with their collection of garbage upstream
the rivers using nets. They have deduced that annually, the net collects
approximately 10,000m3 of materials, including wood (60%), plastic packaging
(35%) and ‘other’ waste (5%). This potentially highlights the importance of
teaching classification systems to identify wood types and other organic
matter that might get washed down rivers but could eventually biodegrade.

Savastano et al. [2021] used Synthetic Aperture Radar (SAR) to detect plastic
marine debris in the Balearic Islands. The study trains the AI model on a
dataset of pixels, labelled as either plastic or non-plastic utilising three
different supervised classifiers; RF; SVM and Gaussian Naive Bayes(GNB). The
authors do not make any concluding remarks on the SVM or RF models but did
discuss that upon visual inspection; the GNB performed the best on unlabeled
images, and that GNB produced a smaller amount of false positives however,
when applied against the validation dataset, its performance on labelled areas
had the lowest balance accuracy of 0.68 whilst RF and SVM achieved 0.86. The
conflicting and inconclusive results of this experiment would suggest that
further research on the most appropriate models to be used, would be
beneficial, perhaps an application of explainable methods would determine the
reasoning behind the level of false positives and provide an opportunity to
improve the results.

In a hybrid method of both tracking and detecting debris; Kikaki et al. [2022]
produced a method presenting two weakly supervised semantic segmentation
tasks; RF and U-Net. The RF model used spectral signatures of input features
(spectral indices, pixel, textural features) extracted from grey-level co-
occurrence matrices (GLCM). To take the Sentinel-2 multispectral images as an
input; the authors used a U-Net model which output a binary mask that was able
to indicate if debris was present or not. To test their benchmark dataset;
Kikaki et al. [2022] achieved high accuracy scores and concluded that the
MARIDA set is a valuable resource for developing and evaluating ML algorithms,
when using Sentinel-2 remote sensing. Their RF model achieved an overall
accuracy of 93%, with a F1 score of 0.68 whilst, the U-Net CNN achieved a high
accuracy of 91%, with a F1 score of 0.60. The performance of the models varied
between the classes but their results for the Marine Debris class had a strong
outcome. The use of one class to represent all floating debris could cause
identification issues due to the vast array of structures and objects this
classification represents and it is worth mentioning that with U-Net the
marine debris achieved a 0.7 precision-recall average score compared to the
best performing version of RF at 0.92.

Sannigrahi et al. [2022] developed a nonlinear kernel Normalised Difference
Vegetation Index (kNDVI) for detection of floating plastic in the ocean. They
produced two machine learning models; SVM and RF. The SVM method used the
kernel; radial basis function (RBF), however, the authors concluded that RF
produced more promising results, with a higher accuracy; less errors; and less
uncertainty when classifying plastic pixels from satellite data. Within the RF
model, the authors used four remote sensing spectral indices; Floating Debris
Index (FDI), Normalised Difference Vegetation Index (NDVI), Plastic Index (PI)
and (as mentioned above) the kNDVI. The study also conducted hyperparameter
tuning such as the number of trees, splits at each node, cost factor and the
number of support vectors, which optimised their performance. The authors
suggest that slightly weathered plastic objects may cause the machine learning
results to fluctuate, particularly as plastic reflects light differently
depending on thickness, colour or degradation. Despite this, their RF model
still achieved 88% and 94% accuracy on their two real world test sites; Beirut
and Calabria. They conclude by remarking that remote sensing and machine
learning techniques are a plausible combination for detecting marine debris.
Based on their research, future studies should take into consideration the way
in which plastic and other debris degrades over time, as it is possible this
will affect classification as well as tracking results.

###  3.2 Classification & Object Detection

Fulton et al. [2019] evaluated four popular algorithms Tiny-YOLO, Faster-RCNN
(with Inceptionv2), YOLOv2 & SSD (Single Shot Multi Detector with MobileNetv2)
in real time, to compare the results for detection of marine debris -
particularly plastics. The authors found that all four models performed well
on their image dataset. Their results were also compared on a NVIDIA GTX GPU
1080, NVIDIA Jetson GPU TX2 and Intel™ i3-6100U CPU, to assess real-time
performance which varied in frames per second (fps), per model on each
machine. Faster-RCNN outperformed the other models in terms of accuracy with a
81% mAP. The authors comment that they believe Tiny-YOLO and YOLOv2 had a
better balance between speed and accuracy; their mAPs were as low as 31.6% and
47.9% respectively, their analysis however makes more sense when the plastic
detection average precision is taken into account at 70.3% & 82.3%
correspondingly, when compared to Faster RCNN’s plastic precision score of
83.3%, it seems more feasible, as particularly TinyYOLO was significantly
faster when using the 1080 GPU, at a rate as fast as 205 frames per second
compared to Faster RCNN’s fps of 18.75. Finally SSD scored lowest in terms of
detection; 67.4% mAP with a 69.8% precision score in the plastic category, it
was slowest when using GPUs, however it outperformed the other models in speed
when used on the CPU which would operate best on low powered AUVs. Therefore,
those who have limited processing power and time may opt in for SSD as an
efficient model, though the results would be outperformed by Faster-RCNN.

Watanabe et al. [2019] explored the use of unmanned aerial vehicles,
autonomous underwater vehicles and other autonomous robots to produce a high-
spatiotemporal marine-monitoring system. In this study they use the YOLOv3
algorithm for object detection in marine environments. Using this
architecture, they found that they were able to detect objects almost in real-
time. When tested on underwater sea life, their model achieved a mAP of 69.6%
and underwater marine debris had a mAP of 77.2%. However, this was built and
tested on a visual processing unit (VPU), so they felt they would need to test
on various other CPU and GPUs.

Kylili et al. [2019], needed several thousand images to optimise the
parameters of their algorithm. As they ran their simulation through training,
testing and validation, they required a different set for each. They ran 50
training epochs and achieved incredibly accurate results. The training set was
$\approx$100% accurate with about a 1% loss, the testing set was $\approx$99%
accurate with $\approx$4% loss and their validation set achieved 86% in
accuracy.

The Visual Geometry Group-16 (VGG16) model made use of transfer learning as it
was pre-trained on ImageNet. A class activation map highlighted the areas
within the image that the model was classifying which helped the authors
validate their results.
Unfortunately, though it is difficult to measure; Ferries [2023] estimates
that floating debris only makes up 15% of the predicted amount of debris in
the oceans. Therefore, research depicted on floating debris may not have the
desired impact. The authors particularly focused on the credibility of their
results (a theme that most authors have omitted within this survey) by
reinforcing their results accompanied by the loss percentage as well as
altering the structure of their model using three different scenarios.

By applying a Bottleneck (BM) technique to VGG16, Kylili et al. [2020] were
able to increase their validation accuracy on detecting floating debris on the
Cypriot coastline up to 90%, as they amplified their dataset and number of
classes. They found that even from low resolution images, VGG16 was able to
accurately identify marine debris. The authors particularly note the
importance of data augmentation to expand the dataset, with that aside, they
feel this research could be successfully used for monitoring and managing
marine litter. In their conclusion, they suggest applying YOLOv3 in real time
to video data.
They continued their work [Kylili et al., 2021] by collecting shore samples
along six different Cypriot beaches and using YOLOv5 and YOLACT++ to classify
and localise the debris in the images. From this research they were able to
deduce that there would be approximately 66,000 plastic items scattered across
Cypriot beaches. They also discovered that the majority of debris sizes ranged
from 10cm to 30cm - this potentially offers plausible representation of
parameters needed for future researchers. To conclude their findings; they
determine the importance of “intelligently [monitoring] litter over large
areas of interest in a short period of time” and how that is an integral
contribution to marine debris solution.
The authors continue to research deep learning methods on debris via both
detection and tracking, in a study by Teng et al. [2022]. To estimate the
abundance of debris and track objects, they first tracked the data using the
centroid tracking technique. They were able to produce a mean average
precision of 72% on a pre-trained YOLOv5 model with the MSCOCO dataset and
achieved a classification accuracy of 89%. Teng et al. [2022] proposed that
their method could be used as a more cost effective method to manual surveys
for estimating the quantity of debris. They believe their findings could be
integrated into other digital applications or other remote methods of
surveying, which may enhance its performance. The authors also suggest that
this method has the ability to bring the current technology on accurately
mapping plastics in the sea, forward to more successful results, which
ultimately would help humanity formulate more efficient strategies when
addressing any method of conservation efforts within water bodies.

In a study scanning large areas of the sea surface, comparing the results of
both Faster R-CNN (FRCNN) and YOLOv5 on their own collected dataset; de Vries
et al. [2021] found that even though FRCNN used transfer learning with the
COCO dataset [Lin et al., 2014]; YOLOv5 had a better performance of detecting
small objects (YOLOv5: 0.15m, FRCNN: 0.35m). The authors declare that “the
detection of the smallest objects is irregular”. To further develop the
reliability of their results, they suggest utilising concurrent datasets;
additionally the authors expressed the need for gathering further footage.
Consequently they disregarded their study as findings but rather as a
methodology proposal - though some details on the accuracy of their
predictions would have helped with other model comparisons. Their work does
however highlight the crucial aspect of the size range of undersea objects - a
phenomenon researchers will need to closely observe when collecting and
classifying data.

Deng et al. [2021]’s study focuses on the task of marine debris detection and
instance segmentation. The authors produce a fortified variation of Mask R-CNN
by incorporating dilated convolution in the Feature Pyramid Network, spatial-
channel attention mechanism and re-scoring branch. They were able to upgrade
the feature extraction and enhance the accuracy of the instance segmentation
to a mAP50 of 59.2% - a 2.5% increase from the standard Mask R-CNN trained on
the TrashCan dataset. Three other algorithms were used to compare the results
and they under performed in comparison: 55.9% (SOLO) and 57.1% (CondInst)
within instance segmentation. Within object detection, their mAP50 reached
65.2% with improved lateral connection - a 9.5% increase to the original
structure, and again outperforming competing models such as Faster-RCNN
(55.4%), Retina-NET (57.3%) & FC0S (60.4%). Their research particularly
exaggerates the importance of reinforcing available models, especially within
a specialised, vast field such as marine debris and how the continuous
evolution of models is crucial for deep learning development. The downside to
this model is its heaviness, limiting its ability to detect in real time but
the authors state their intention to develop an optimised, lightweight
version.

Hipolito et al. [2021] researched the use of deep transfer learning with the
YOLOv3 algorithm, for the detection of underwater marine plastic debris. The
researchers utilised a very small sample size of the Trash-ICRA19 Fulton et
al. [2020] dataset of underwater plastic waste to train the deep learning
system. The study achieved high training and validation accuracy, with an mAP
value of 98.15%. The authors declare their sample size to be small - to
produce definitive, conclusive results, their observation is arguably true and
the authors do not seem to explain their choice for disregarding the full set
to obtain a small sample size. Had they used the full version of Trash-ICRA19,
then for a binary classification set of 8,580 images, this study would hold a
larger dataset than approximately two thirds of the other papers we have
evaluated in this review.

Xue et al. [2021a], Xue et al. [2021b] published two studies in the same year
that developed enhanced classification methods to identify and categorise deep
sea debris. In one study [Xue et al., 2021a], they proposed a novel network
model called Shuffle-Xception. It uses a hybrid combination of depthwise
separable convolutions, shuffle operations and shortcut connections to enhance
the network’s classification performance. The authors compared Shuffle-
Xception with ResNetv2-34; ResNetV2-152; MobileNet; LeNet & Xception. On all
seven classes; they found their F1 stop was higher on Shuffle-Xception and
their precision scored an average of 95% suggesting that Shuffle-Xception is
an appropriate option for deep-sea debris classification - particularly when
considering subject variety and its effects on detection models. In their
other publication [Xue et al., 2021b], the authors proposed a one-stage
network called ResNet50-YOLOV3; when compared against other detection
networks, their results outperformed them in both accuracy and speed with a
mean Average Precision (mAP) of 83%.

Moorton et al. [2022] compared the VGG-16 algorithm with a custom
convolutional model on the same dataset which is curated by them. The
customised CNN consisted of three convolutional layers with 32 nodes and no
dense layer, whereas the VGG-16 model used ImageNet transfer learning weights.
Consequently VGG-16 scored 95% accuracy and the CNN model scored 89%, however
the authors convey the need for a larger database with more diversity.

With a focus on exploring under the surface debris, Sánchez-Ferrer et al.
[2022] have produced a dataset suitable for training machine learning models
on underwater data. To test the results of their database. They chose to use
Mask R-CNN due to its promising ability to detect and segment objects within
an image. The model hyperparameters were optimised to improve its performance.
The confidence acceptance threshold was modified to 50% from 70% which
produced a bias of the model towards an overestimation. The authors voiced
concerns that the environment conditions of underwater data drastically
changed and that it is a challenge to represent the progressive degradation
found in underwater data. They tested their model on two underwater videos;
one within a controlled fishbowl environment and one within a real-world
seabed. They discovered the complexity of the latter negatively impacted their
results and consequently, have recommended a greater variety than their data
set.
Furthermore, the researchers discovered confusion between categories, such as
“square can”, “basket” and “metal debris”. These classifications were easily
misinterpreted by the framework. Additionally, they found that some categories
such as “shoe” were showing large confusion rates due to being
underrepresented, highlighting the importance of a balanced dataset. The
research also concluded that there were False Positive errors within images
that contained more than one single object to detect and that the algorithm
favoured the easiest option. They believe this is due to mislabeled objects
and where the full object is not visible (partially offscreen or overlapped).
The model performed fairly well, with a mAP of 60% but the authors acknowledge
that the size of the CleanSet dataset is limited, does not contain enough
variation in debris shapes and colours but also has only been accumulated in a
controlled environment.
Using Mask R-CNN, Sánchez-Ferrer et al. [2023] used the same dataset with
slightly altered classes to progress the recognition performance. The study
aimed to explore enhanced techniques that can be used for application on
automated vehicles - to achieve this, they fortify their dataset with
augmentation and conclude that synthetically produced data is able to boost
the overall performance of object detection over the use of real data alone
and were able to achieve a mAP of 63.5% (Instance) and 65.2% (Material). The
research paper does however point out that alternative data generation methods
should be explored for more accurate real debris object detection.

Zhou et al. [2023] aimed to address the challenges of small scale and
occlusion of marine debris by developing an object detection network called
YOLOTrashCan. YOLOTrashCan consists of two main components; a backbone network
which enhances feature extraction and a feature fusion network which combines
features from different scales to elevate detection accuracy. The authors
found that their model produced a detection accuracy of 65.01% on the
TrashCan-Instance dataset (their Material dataset produced slightly lower
results of 55.66%). They were also able to reduce the network size by 30MB,
which makes it a more efficient model for practical applications (such as
cleaning marine debris).
In the same year, using the same dataset; Liu and Zhou [2023] published their
findings when they proposed a modification of YOLOv5s on marine debris
detection by replacing the backbone with MobileNet and introducing an
attention mechanism for filtering key features. Their results found they
achieved a 4.5% mAP increase from the original YOLOv5 model, achieving a mAP
of 67%, whilst simultaneously meeting the requirements of real-time detection.
Interestingly, this model outperformed YOLOTrashCan [Zhou et al., 2023], which
could refer to the model type but also the increase in classifications that
the authors applied to this study.

From the comparison of the available research on detection within marine
debris, the results are generally positive with high mean average precision
scores. The next section briefly explores similar work on river debris and why
it is examined within our survey paper.

###  3.3 Deep Learning for River Debris Identification

The exploration of addressing river debris is highly important as it is
estimated that more than 88-95% of the input of plastic carried into the sea
comes from only ten river systems. [Schmidt et al., 2017], [Jakovljevic et
al., 2020]. Part of the problem of addressing marine debris is to prevent it
from entering in the first place, therefore, a major contribution to resolving
this issue is to intercept river debris.

van Lieshout et al. [2020] used deep learning to develop a method of
monitoring plastic debris in rivers. In their study they used two different
CNN’s for different aspects of the research as they wanted to develop the
current plastic-waste monitoring methods. To achieve segmentation they used
Faster R-CNN. However for the detection, Inception v2 (pretrained on COCO) was
trained on the output of the segmentation stage. This helped to detect the
regions of the image that contained plastics. The authors logged each of the
changes they made to advance the precision score. To begin, they presented the
effects of augmentation application on their data. With no augmentation, they
strengthened their score of 59.4% by adding flipping methods which brought
their precision rate to 63%. They then altered their optimiser from momentum
to Adam and found their rate reinforced to 65.7%. Finally, the authors changed
their learning rate from fixed to adaptive, which gave them their overall
precision score of 68.7% - a 9.3% transfiguration from their initial score.
The authors found that this research is impacted by the location diversity for
the data. To test their method against a ground truth option; they used human
counters to monitor their methods and found their technique received similar
results. Human labelling reliability can be fickle, which could degenerate the
monitoring performance, as humans labelled the 280,000+ objects within the
data.

Conducted in two different locations; Laos and Thailand. Maharjan et al.
[2022] proposed a method of mapping debris on rivers using an autonomous
unmanned vehicle by obtaining georeferenced ortho-imagery and combining it
with deep learning to detect river plastics. They created two separate
databases of floating plastics in rivers, which they trained and tested on
four different algorithms from the YOLO family; YOLOv2, YOLOv3, YOLOv4 &
YOLOv5. To robustly test the differences, the authors compared the results of
the accuracy, time and computing resources. Interestingly, they found that
bright, rigid plastics obtained the highest accuracy and that results of
debris covered in sediment suffered; “plastic filled with sand and soil or
affected by shadow are ignored”. They also revealed that within shallow
waters, the visibility of the water made detection difficult. Atmospheric
conditions with data collected from height such as UAV and satellite should be
considered to minimise the effects of temperature and wind speed. Overall, the
authors suggest optimal weather conditions for high quality data. Due to a
discrepancy of light absorption and reflection within differing layers of
underwater data - the authors suggest incorporating hyperspectral sensors to
help distinguish plastic from other materials. Due to the 83% mAP and size,
they have concluded that the pre-trained YOLOv5s model performs best. The
authors released a Phantom 4 drone with 4K resolution camera to sample the
distance of approximately 0.82cm to assess the monitoring methods of plastic.

###  3.4 Alternative applications

Applications of 3DCNNs in this field so far have only been used within remote
sensing sea ice image classification [Han et al., 2020]. Due to the expert
knowledge needed within sea ice classification and the manual labelling
limitations; this paper is designed to address the problem of small sample
sizes. The method called SE-CNN-SVM uses a hybrid network extracting features
from hyperspectral images and applies them to a fully connected layer and
softmax layer for classification, integrating a squeeze and excitation (SE)
block and a support vector machine (SVM) classifier. The results outperformed
several classical methods including random forest, decision tree, naive Bayes
and k-nearest neighbours; particularly when using small sample conditions.

Though the research is based on washed up debris rather than within the marine
environment, the work by [Fetisov and Chubarenko, 2021], proves it is still
worth exploring, as they were able to determine that a westerly wind was
bringing the debris to the Sambian Peninsula in Kulikovo, Kaliningrad, through
the use of a decision tree, an unspecified ANN (artificial neural network) and
some geographical knowledge (amber deposit wash-outs) This example of research
can help us track which tides are carrying debris, if we can track the source
then we can implement robust methods of preventing influxes from entering the
sea and potentially also have a better idea of the type of debris we are
likely to be detecting. The authors conclude that their data is not of a
sufficient size for training an ANN but highlight the advantages of these
predictions, particularly on anticipating rip tides for coastal regions as
they believe it might coincide with marine wash-outs.

The authors Chang et al. [2020] tried to address the growing marine debris
concerns on Jeju Island to produce a clean coast detector. They augmented
their data to successfully classify their images which they believe is how the
Xception model performed so well, as the training set scored 97.73%. The
application is aimed at informing tourists on the cleanliness rate of the
coasts to help promote sustainable tourism - as 80% of all tourism is related
to coastal regions and is the lifeblood of many countries (World Resources
Institute. 2021); their efforts to reduce marine waste should be considered
for tourism and economical conservation.

###  3.5 Computational Cost and Performance

To further understand the speed and performance of some of the models, it is
important that researchers disclose the hardware they use. Some authors have
shared this information and in this section we have displayed some of the
specifications that researchers have provided on Table LABEL:table:_1, to give
the reader the option to study the meta data with more of a visual overview.

Table 1: Comparison of models, performance, sample size and hardware use of most recent computer vision models. Ref |  Model |  Data |  Results |  Hardware
---|---|---|---|---
Acuña-Ruz et al. [2018] |  SVM & RF & LDA |  144 samples |  SVM:80%, RF:77%, LDA:70% |  PS-300 from APOGEE ® (PS300) for the VNIR range, and TerraSpec 4 Hi Res from ASD Inc. (TS4) for the VNIR-SWIR range
Kylili et al. [2019] |  VGG-16 |  12,000 images |  Validation accuracy 86% |  Intel Xeon (CPU 2.40 GHz) processor with NVIDIA (Quadro K4200) graphics card
Fulton et al. [2019] |  TinyYOLO, YOLOv2, SSD, Faster RCNN |  5,720 images |  Faster RCNN mAP: 81% |  GPU (NVIDIA GTX 1080); Embedded GPU (NVIDIA Jetson TX2); CPU (Intel i3-6100U)
Watanabe et al. [2019] |  YOLOv3 |  189 (debris) 8,036 (bio) |  mAP Sea life 69.6% Debris 77.2% |  Intel® CoreTM i7-7800X CPU 350 GHz, 64 bits, 40-GB RAM, GPU Nvidia GTX 1080, CUDA 9.0, cuDNN 7.0.3, and OS ubuntu 16.04
Chang et al. [2020] |  XCeption |  3,880 images |  97.73% accuracy on training set |  -
Jakovljevic et al. [2020] |  ResUNet50 ext of UNet |  3 Datasets: 328; 434; 1,846 images |  F1 Score Average 0.89 |  DJI Mavic pro equipped with an RGB camera
Kylili et al. [2020] |  VGG16 |  1,600 raw; 32,000 images |  90% val accuracy |  Intel® Xeon® machine equipped with an Intel CPU Core E5-2630 v3 (2.40 GHz) with 48.0 GB of memory (RAM) and an NVIDIA Quadro K4200 graphics card, clock-rated at 784 MHz with 28.6 GB of memory
Panwar et al. [2020] |  RetinaNet (Resnet50 backbone & FPN) |  369 images |  mAP 81% |  -
van Lieshout et al. [2020] |  Faster R-CNN and Inception v2 |  Raw 1,272 jpg |  Precision: 68.7 |  -
Han et al. [2020] |  SE-CNN-SVM |  3,190 samples |  97.42% |  Intel Core i5-4590 with 3.30 GHz and Nvidia GeForce GT 705
Savastano et al. [2021] |  RF; SVM; GNB |  8,395 pixels 1,794 plastic pixels |  Balanced acc: 0.86;0.86;0.68 |  -
Kylili et al. [2021] |  YOLOV5 & YOLACT++ |  1,650 images |  AP 92.4%; 69.6% |  NVIDIA® Tesla® K80 graphical processing unit (GPU)
Xue et al. [2021b] |  ResNet50-YOLOv3 |  10,000 images |  mAP 83.4% |  GeForce GTX 1080Ti GPU with- a capacity of 11 GB
Deng et al. [2021] |  Mask RCNN |  7,212 images |  mAP 59.2%; 65.2% |  Ubuntu18.04. Intel(R) Xeon(R)Silver 4110 CPU @2.10GHz. GeForce RTX 2080Ti. CUDA 10.2.
Xue et al. [2021a] |  Shuffle-Xception |  13,914 images |  95% precision average |  GeForce GTX 1080Ti GPU and installed with Intel(R) Xeon(R) W-2133 CPU at 3.60 GHz, 31.7 GB RAM
Hipolito et al. [2021] |  YOLOv3 |  300 images |  mAP 98.15% |  -
Fetisov and Chubarenko [2021] |  ANN |  25 cases |  - |  -
de Vries et al. [2021] |  Faster R-CNN-Ultralytics YOLOv5 |  18,589 images |  - |  GoPro Hero 6 Black
Teng et al. [2022] |  YOLOv5 |  2,050 images |  mAP 89.4 % |  NVIDIA® Tesla® K4 GPU from Google Colab
Maharjan et al. [2022] |  YOLOv2; YOLOv3; YOLOv4; YOLOv5s |  $>$500 tiles |  mAP 77%; 81%; 83%; 83% |  (1) Anaconda with Jupyter running on a personal computer with an Intel®CoreTM i7-10750H CPU @2.60 GHz, 16GB RAM, and NVIDIA GeForce RTX 2060 GPU with 6 GB GPU RAM, and (2) Google Co-laboratory Pro. The personal computer was used for YOLOv3 and YOLOv5, and GoogleColaboratory Pro was used for YOLOv2 and YOLOv4
Sannigrahi et al. [2022] |  SVM & RF |  27 Sentinel-2A/B scenes |  88% & 94% accuracy |  -
Moorton et al. [2022] |  VGG-16 & custom |  1,744 images |  accuracy of 89% CNN and 95% VGG-16 |  Dell Inspiron i7-7700HQ CPU 2.8GHz, 16GB RAM, 64-Bit with a Nvidia GeForce GTX graphics card, running on Windows 10
Kikaki et al. [2022] |  Random Forest model (RF) & U-Net architecture |  1,381 patches; 3,399 marine debris pixels |  92%; 70% accuracy |  -
Sánchez-Ferrer et al. [2022] |  Mask R-CNN |  1,223 images |  mAP 60% |  Intel(R) Core(TM) i7-8700 CPU @ 3.20 GHz with 16 GB RAM, a NVIDIA GeForce RTX 2070 with 6 GB GDDR6 Graphics Processing Unit (GPU) with the cuDNN library
Sánchez-Ferrer et al. [2023] |  Mask R-CNN |  1,223 images |  mAP Instance 63.5%; Material 65.2% |  -
Liu and Zhou [2023] |  YOLOv5 modified |  7,212 images |  mAP 67% |  Intel(R) Xeon(R) Silver 4210R<EMAIL_ADDRESS>with an NVIDIA GeForce RTX 2090Ti GPU
Zhou et al. [2023] |  YOLOTrash-Can |  7,212 images |  mAP 58.66%; 65.01% |  AMD Ryzen 7 3700X, Nvidia TITAN RTX 24 GB, and RAM 48G

##  4 Data Acquisition

Conclusions across various marine debris studies, display a common
recommendation as researchers and scientists declare that there is not a
public database available to solidify their results. Therefore, this section
of this survey explores the various methods of collecting data, augmenting,
labelling and classifying, that scientists in the previous five years have
used.

The JEDI (JAMSTEC E-Library of Deep sea Images) dataset will get mentioned a
few times within this section. It is a part of the JAMSTEC (Japan Agency for
Marine-Earth Science and Technology) data collection and is popular in many
studies, as so far it has been the most comprehensive open access collection
of sea debris and biodiversity footage underwater [JAMSTEC., 2009]. These deep
sea photos and videos are taken from a submarine off the Japanese coast with
varying conditions. However, the downside to this database is that it is
available for multiple uses rather than specifically for machine learning.
Therefore, there is a lot of data cleaning - some images or footage contain
parts of the ROV system in place, the quality is not always to a computer
vision quality standard and the data would need to be labelled appropriately.
Hence also why this dataset has not been included in 4.1 Benchmark Databases
and alternatively, is mentioned here.

When detecting debris on the beaches of Acuña-Ruz et al. [2018] used an
interesting method of collecting data through fieldwork, lab analysis and
satellite data. To produce a ground truth dataset, as well as a dataset used
to create a predictive model for the classification; the fieldwork collected
144 samples of various macroplastics and organic material. All of the samples
were characterised, labelled and where possible, geo-referenced. These were
moved to be analysed in the lab later. Within the laboratory the team were
able to observe the spectral signatures of the collected samples using a
hyperspectral sensor. From each class; the researchers selected 90 pixels from
the scene to develop a supervised classification predictive model which was
split at a 70:30 ratio. The other dataset consisted of satellite imagery with
a spatial resolution of 0.31m to classify the debris on the beaches they were
observing. Due to atmospheric effects on satellite imagery (such as weather),
the authors were able to use DigitalGlobe to correct the effects.

de Vries et al. [2021] collected 4,000 images with a GoPro attached to a
vessel. They applied random transformations including “image shear along the X
and Y axis; zoom in/out; rotation by an arbitrary angle; slicing of images to
produce different scales; translation; random exposure and contrast [and]
multiplication of the digital number (DN) values”. This resulted in a dataset
yielding 15,227 images. For such a large database, they sought out an online
labelling platform called Zooniverse [Zooniverse, 2023] which the authors
chose to categorise into three classes: hard plastics, nets, and ropes - this
data was was cleaned using a VGG annotator and they were careful to only label
clearly identifiable debris. Afterwards, the data was combined into one class
of ‘debris’ as the authors claim it improved the detection precision. The
decision to limit false positive detections by including an additional 3,362
photos of the sea without any debris which exhibited a variety of sea states;
brought their training set to 18,589 images and their validation set of 739,
what’s more, the extra data functioned as a means of quality control.

J-EDI was used for research by Fulton et al. [2019] who collected 5,720 images
drawn from video data and annotated them with the LabelImg software. Their
dataset Trash-ICRA19 [Fulton et al., 2020] was divided into three
classifications including Plastics, ROV (containing all synthetic objects
intentionally placed) & Bio and their test set consisted of 820 images. Due to
the overwhelming assortment of marine debris, they chose to focus on plastics
as it is the most harmful material in the ocean currently. Regardless of the
difficulty of obtaining this data, their set - of which they describe to
closely mimic the real world - has a high standard of environmental variety
factored in, due to the range of light conditions, overgrowth, states of decay
and occlusion.

Using a GoPro Hero 6 and scuba diving off the Japanese coast, Watanabe et al.
[2019] recorded a 6m visibility at 12m depth noting that it was cloudy, to
collect imagery of underwater floating debris. They also used three cameras to
photograph debris along the beach as well as downloading imagery from Google
Open Images. Using a different detection model for both marine biodiversity
and debris: for three categories of fish, turtles and jellyfish; they used
6,908 images for the training and 1,127 for testing at roughly a 86:14 ratio
split. The neural network used for debris split the data into plastic bottles,
plastic bags, driftwood and other debris; due to unavailability of obtaining
much underwater data; this model was trained with a much smaller collection of
189 images and just 37 images for testing - a better ratio (80:20) but room
for bias and consequently the set too small for debris detection.

Much like the previous work mentioned, the set produced by Chang et al. [2020]
collected a small dataset that would not provide enough information for
computer vision training. However, their method of collecting data was to
select and download 194 Google images which they split into the binary
classification of ‘clean’ and ‘polluted’ beaches. Due to the small size of the
database, after removing irrelevant files that overlapped; the authors
labelled 97 training images and used 20 images within the validation set. To
test the model they used the remaining 10 images to conclude their overall
database at a quantity of 127 images.

Spanning two sites in Lake Balkana; Crna Rijeka and Vrbas Rivers, the authors
Jakovljevic et al. [2020] collected high resolution orthophoto image data.
Using a DJI Mavic pro equipped with an RGB camera, six AUV surveys at a height
of 12 - 90 metres were conducted. To preprocess the images into a higher
quality, the authors used the Structure from Motion (SfM) algorithm. The
floating debris most analysed were three variants of plastic; OPS, Nylon and
PET. The authors ability to track floating plastic could be a beneficial
addition to monitoring debris trajectories in the ocean.

When training their dataset for river plastic monitoring; van Lieshout et al.
[2020] captured a dataset of objects floating on the river by camera. The
authors used the data to train a CNN which organised the images into a binary
classification of either plastic or non-plastic. To collect their data; they
recorded a video of floating debris on the river for 26 days; of which 1,272
JPEG images were extracted to create the river image dataset. These were
collected over five different locations. To create the floating plastic
dataset; they uploaded their set to be labelled by citizen scientists on
Zooniverse [Zooniverse, 2023], which were later inspected and corrected where
necessary. Out of the labelled 280,832 objects, 14,968 boxes were floating
plastic. The data was split into their binary classification and then was
further divided into five subsets of the locations they shot their footage at.

Marine & Carbon Lab, are the organisation in the University of Nicosia who
have co-produced multiple studies in the field of detecting marine debris and
were able to pretrain their VGG16 model on ImageNet and then continue training
the model with their own previously curated dataset of floating marine debris
as well as some donated images from a nonprofit organisation called Algalita
(https://algalita.org/). In their 2019 study, each of the three categories of
floating debris; plastic bottles; plastic buckets; plastic straws contained
250 images in the research; however, data augmentation in the form of zooming,
rotating, shifting and flipping increased this number to 4,000 images each.
The set was then split into 3,200 train images and 800 for testing. The next
year, Kylili et al. [2020] separated their data into eight classes; six for
synthetic debris which included ‘plastic bottles’, ‘plastic buckets’, ‘plastic
bags’, ‘fishing nets’, ‘plastic straws’, and ‘food wrappings’. Additionally,
they chose to integrate a class of ‘Flying Fish’ and also an ‘Other’ category.
Each class contained 200 images, using augmentation, they manipulated each
image 25 times to create a class of 4,000 images and an overall dataset of
32,000 images; the classes were then split to an 80:20 ratio for testing and
training.
Following this research, the team and another researcher further developed
their study. Teng et al. [2022] collected a database consisting of 2,050
videos split between nine categories of plastic bottles, plastic buckets,
plastic bags, fishing nets, plastic straws, food wrappings, a fish species,
aluminium cans and cigarette butts. The data was collected from similar
sources such as ImageNet and Algalita, but their YOLOv5 model was pre trained
on MSCOCO. To annotate their database the authors used the YOLO_mark and Make-
Sense tools [Skalski, 2019], which were used as a method to manually draw the
ground truth bounding boxes. The authors declare that their data is available
upon request.

Hipolito et al. [2021] obtained their data from the Trash-ICRA19 dataset
Fulton et al. [2020]. The authors collected a small sample size of 300 images
split into the 80:20 ratio and relabelled the set to Non-Bio to keep it as a
singular classification system. Hipolito et al. [2021] claim the original
dataset held two classifications; bio and non-bio split equally with 4,290
images per category, totalling the collection at 8,580 images, however at the
time of writing this paper, we found that the same dataset currently consists
of 5,700 images categorised into three main classifications and the original
paper referenced Fulton et al. [2020] states 5,720 images. We are not
confident if this is a citation error or if the original dataset has since
been adapted in some way such as employing further data cleaning and/or data
augmentation techniques.

Between the two studies; Xue et al. [2021a] & Xue et al. [2021b], collected a
relatively similar database from the underwater open source collection that is
provided by the Japan Agency for Marine-Earth Science and Technology
(JAMSTEC). The authors extracted the appropriate frames they needed from the
archive of footage with only a single object within each frame, using
‘LabelImg’ [Tzutalin, 2015] to label their dataset of which they have named
3-D dataset (not to be confused with three dimensional); this particular
collection of data consisted of 10,000 images [Xue et al., 2021b] \- their
other study consisted of 13,914 [Xue et al., 2021a]. Both databases were
organised into seven categories; cloth, fishing net & rope; glass; rubber;
plastic; natural debris and metal - though the training set is comprehensive;
the classes are unbalanced in Xue et al. [2021a] ranging from the smallest
sample size : 1,825 (Natural Debris), to the largest: 2,234 (Fishing Net &
Rope). The DDI dataset was divided into three subsets; 70% training, 15%
validation and 15% testing.

Though the amount of data collected is not specified in the paper; Maharjan et
al. [2022] used the YOLOLabel tool to record the bounding box for each piece
of plastic in each image they had collected of river debris; this resulted in
annotating 500 tiles for each river. The labelled objects were then classified
into a binary group of either plastics or non-plastics.
By contacting a variety of organisations and dive schools around the globe;
Moorton et al. [2022] were able to collect and label a small database of 1,644
underwater images using augmentation and 100 testing images. The dataset
exhibited diversity in lighting and environmental factors; it also had a large
scope of biodiversity as well as synthetic debris, however the set was split
into a binary system consisting of Animal (which depicted all biodiversity)
and Litter and also was not split at the standard 80:20 train/test ratio - the
authors found that due to their dataset, the models were making similar
mistakes to turtles when it came to differentiating between jellyfish and
plastic bags or wrappers. The dataset is available upon request from authors.

Sannigrahi et al. [2022] collected their data from two sources. For in-situ
plastic locations; they collected data from Topouzelis et al. [2019],
Topouzelis et al. [2020] in Greece and Themistocleous et al. [2020] in Cyprus
spanning from 2018, 2019 and 2021. Using a three class classification scheme;
the data was split into Plastic Object; Natural Debris (wood) and seawater.
Later, the authors also conducted a fourth class to detect mixed objects -
both plastic and wood. The remote sensing was able to manually detect floating
debris using the FDI. To collect their Multispectral Imagery, they were able
to use data from Copernis Open Access Hub [ESA, 2023]; this database was used
for the remote sensing analysis. They collected 27 Sentinel-2A/B scenes which
were processed with the European Space Agency’s software; Sentinel Application
Platform.

Sánchez-Ferrer et al. [2023] followed their previous work based on the
CleanSea dataset, where they have collected 1,223 images for seventeen classes
including: Plastic bag, Towel, Can, Glove, Packaging, Basket, Bottle, Pipe,
Fishing net, Metal waste, Plastic waste, Square can, Rope, Tire, Bumper, Shoe,
Wood. The set was organised in an 80:20 split with 10% validation. They opted
in for augmented data to amplify the results. The augmentation demonstrated a
combination of transformation methods randomly applied to each image, methods
incorporated were “horizontal flips, mirroring effects, Gaussian blurring,
contrast alterations, brightness modifications, scale changes, translations,
image rotations, and image shearing”, the random application was an intuitive
way of preventing overlearning. A large increase via data augmentation
provided drastically better results.

Liu and Zhou [2023] used a dataset of 7,212 images collected from the
TrashCan-Instance and split their data into 50 class names (of which were not
elaborated on within the paper); though the individual classification are not
specified; the authors describe the categories as underwater observations of
trash, ROV’s as well as a variety of flora and fauna. To have such a large
number of classifications is an exciting step forward in this heavily
underrepresented area of research; however, assuming classes are balanced, it
would mean that each class only contained approximately 144 images. This
therefore, would still not be a quantifiable amount for computer vision
training. Though this is not clearly described within the paper and can be
assessed more closely on the TrashCan-Instance dataset produced by Hong et al.
[2020].

To conclude this section; data augmentation has been instrumental in producing
results and based on the methodology conclusions, we know that the use of
augmentation produces larger datasets that are less likely to be over fit or
obtain bias. Unfortunately, the amount of data and classifications with enough
data within them, still limits the available research, leading to inconclusive
and questionable results.
Though many researchers provide examples of the imagery they have used, unless
otherwise stated within their respective sections; to the best of our
knowledge, most research papers did not provide access to their data or we
were unable to locate them. Hence there is still an open gap of public data
availability in this field.

###  4.1 Benchmark Databases

This section briefly outlines some of the benchmark databases of underwater
debris imagery that researches have collected from.

Kikaki et al. [2022] produced a multispectral Sentinel-2 satellite database of
geo-referenced pixels called Marine Debris Archive (MARIDA). MARIDA was
designed to produce a suitable, labelled data collection as a benchmark for
anyone wishing to build or evaluate a machine learning algorithm for marine
debris detection. The researchers take into consideration the importance of
geographically diverse data and additionally have built their set using eleven
different categories consisting of sea surface debris, biodiversity, ships,
clouds and water states. The set was split into a train, test and validation
set as a 50:25:25 ratio. Rotations and horizontal flips were used to further
expand the dataset with augmentation, bringing their overall dataset quantity
to 1,381 patches, that included 837,357 annotated pixels, of which 3,399 were
marine debris. Uniquely, their dataset is able to distinguish between various
marine features that coexist; such as sargassum microalgae, ships, foam, and
other natural organic materials. The authors focused on the annotation
regulations with precision by drafting three experts to label the set, and an
inter-annotator agreement to regulate the annotations.

TACO (Trash Annotations in Context for Litter Detection) produced by Proença
and Simões [2020] is an open source database that collects annotated images of
urban trash. Panwar et al. [2020] adapted some of this database for marine
debris detection by producing the dataset AquaVision which consisted of 369
underwater annotated images from TACO. The authors of AquaVision categorised
the images into four: glass; metal; paper; plastic. Though the sample size is
small, the authors still obtained a mAP of 81% - even when tested on more
random images. The authors claim the small dataset provides “results in a more
efficient manner”, however due to the size and limited categories, it would be
interesting to test how the model and data would perform on a larger scale
with more complex tasks such as avoiding misidentification between plastic and
jellyfish.

The dataset TrashCan used by Zhou et al. [2023] was created by Hong et al.
[2020] pulled from JAMSTEC videos and the authors collected 7,212 underwater
RGB stills. The TrashCan dataset is separated into two parts; Instance and
TrashCan-Material. Material contains sixteen categories such as animal_crab,
plant, trash_fabric, a full list is given in Table 2. The Instance dataset
contains the same categories but has removed the ‘trash_’ sub-categories and
altered them to include a finer list of categories totalling twenty two
classifications as listed under the header ‘TrashCan-Instance’ in Table 2. As
we can see from comparing the lists, the Instance dataset details their trash
categories in more depth and ultimately that could be the result of why its
performance is stronger than the Material set. The Instance set was also used
in the study [Deng et al., 2021], to improve their model of Mask R-CNN in
object detection and instance segmentation. They also applied data enhancement
such as image rotation and cropping to ensure effective feature extraction but
the overall set number was not disclosed.

Table 2: TrashCan dataset classifications TrashCan-Material | TrashCan-Instance
---|---
animal_crab | trash_metal | animal_crab | trash_can
animal_eel | trash_paper | animal_eel | trash_clothing
animal_etc | trash_plastic | animal_etc | trash_container
animal_fish | trash_rubber | animal_fish | trash_cup
animal_shells | trash_wood | animal_shells | trash_net
animal_starfish |  | animal_starfish | trash_pipe
plant |  | plant | trash_rope
rov |  | rov | trash_snack_wrapper
trash_etc |  | trash_bag | trash_tarp
trash_fabric |  | trash_bottle | trash_unknown_instance
trash_fishing_gear |  | trash_branch | trash_wreckage

The authors of this research used 6,065 images for training the Instance
category with 1,147 in the validation set. On their separate set for Material,
their train set consisted of 6,008 images and 1,204 validation. The authors
believe this provided a comprehensive representation of marine debris and
other objects present. They noted that many of the marine debris categories
had similarities in the object properties but did not disclose what the
categories were.

Another benchmark marine debris dataset was produced by Sánchez-Ferrer et al.
[2022]. The CleanSea dataset is composed of submerged debris and mostly
collected from Marine-Earth Science and Technology (JAMSTEC) data which are
videoed and photographed from submarines in Japan. From JAMSTEC, Sánchez-
Ferrer et al. [2022] selected 1,223 images, which were annotated by bounding
boxes and contours of objects and then were organised into 19 classifications
of debris and tested on a Mask RCNN model.

Although there are attempts of curation and use of image databases with either
floating or underwater marine debris scenarios, the repositories publicly
available are still too undiversified and small to produce the results that
researchers are looking for. Hence curation and making available a benchmark
dataset that is open to all researchers would address one of the most common
obstacles for deep learning in this field.

##  5 Survey Overview and Conclusion

To combine all the metadata of the papers reviewed in this survey paper,
Supplementary Table 1 can be used to cross reference the model performance,
amount of data, classification groups and even the hardware to determine more
accurately which results have produced the most reliable outcomes.
Across the available studies we have observed that there is a major lack in
availability of data for marine debris whether underwater or on the coastal
beaches. Therefore, we decided to test to see if a small dataset could still
produce reliable results on the most successful method of detection that we
observed across the research.

Figure 1: Outcome of highest accuracy test. Figures A and B (Top Row): Animal.
Figures C and D (Bottom Row): Litter.

There is a strong correlation between YOLOv5 and a high mean accuracy
precision. Therefore, we decided to test YOLOv5, as it is one of the most
prominent methods highlighted in the relevant literature as presented in the
previous chapters, (Git pulled from Singla [2021]) on our small humble dataset
to directly compare the results. Our dataset was only a small binary
collection of 140 testing images (70 for each category and an 80:20 validation
split). We chose a binary classification of litter and animal, as two
classifications was the most popular choice across the survey and we wanted to
represent as closely as we could. We trained the data using batch sizes of 16,
32 and 64 and epochs of 50, 100 and 150. The optimal parameter settings was
found to be a batch size of 32 and epoch number of 50 yet we found that with
YOLOv5 we were unable to accurately detect the two classifications; there were
many false positives and negatives however most images including object such
as plastic bags were unable to be detected at all.
As this dataset was limited, we decided to increase the amount of data and
attempt to retrain YOLOv5, to check if it was any more useful. Unfortunately,
even with a significantly larger dataset of 1,093 training images, 269
validation and 30 brand new testing images (with 38 objects, two example
images from each class were shown in Figure 1), we were not able to obtain
favourable results, as shown in Table 3, leading us to believe that a small
dataset is not plausible for use in this area of research. We therefore
highlight that a diverse variety of data and classifications is crucial to the
successful training of object detection on marine debris.

Table 3: Results of testing our custom training set on YOLOv5 Batch Size | Epoch No. | Accuracy of Classification of 38 Objects
---|---|---
16 | 50 | 52.63%
16 | 100 | 44.73%
16 | 150 | 47.36%
32 | 50 | 63.15%
32 | 100 | 42.1%
32 | 150 | 52.63%
64 | 50 | 52.63%
64 | 100 | 39.47%
64 | 150 | 60.52%

##  6 Discussion and Future Direction

In order to provide future work suggestions, we have collected other authors’
recommendations within this survey, as well as concluding with our own ideas
accumulated from this research. This section has been organised into various
categories for easier reference.

###  6.1 Data Collection Refinement

The collection of data within the marine debris topic has proven to
considerably affect the results that researchers are claiming. With many
authors stating that database collections are just not large enough [Maharjan
et al., 2022], [van Lieshout et al., 2020], [Zhou et al., 2023] particularly
of underwater debris, it would make sense for a large database to be produced.
Potentially even the use of multiple databases could be explored, as it is
limited in this area of research.

de Vries et al. [2021]’s research attempted to produce a comparative study
between YOLOv5 and Faster RCNN but the authors decided their dataset was not
large enough; it would be beneficial to obtain a sizeable increase in images
and train the methodology the authors have recommended. Individually the
models have produced high mean average precision scores when detecting debris,
accordingly, a comparison would be a valuable insight.
Furthermore, comparing these object detection results on the same dataset as
classification methods which have also performed well such as VGG-16 and
ShuffleXception would be a meaningful study to determine which method of
computer vision is superior for sea debris detection.

In addition to an increase in image availability, researchers such as van
Lieshout et al. [2020], Hipolito et al. [2021], Maharjan et al. [2022] and
Zhou et al. [2023] have pointed out that the variation of images is also not
up to a high enough standard. Their suggestions range from diversifying
weather conditions, locations and visibility within the water. A few authors
request more optimal conditions based on a higher accuracy but in order to
provide an AI framework that works in multiple conditions, perhaps the
databases should embody more challenging pieces of data to enrich the
robustness of the model.

With the dilemma of debris quantity and variation ever increasing, a high
number of classes should also be available to the dataset, to broaden the
complexity of the model.
Jakovljevic et al. [2020] found that approximately 60% of the debris they
catch in rivers is a wood product or similar; it could be valuable to ensure
that classification systems also include wood materials, to increase accuracy
rates on current models. We believe it would be highly beneficial to train
river classification models on wood types, to prevent mistakes in detection
but also if the algorithm were to be applied to a form of automation,
collecting wood would not need to be a priority as it is mostly a natural and
biodegradable material but it might also help to eliminate precious resources
- with that in mind; potentially harmful types of manufactured woods or those
with coatings could be an area of classification to explore. van Lieshout et
al. [2020] suggest that future research should monitor the reliability of
human labelling; increasing the precision of how we categorise data.

Within their research exploring the use of SAR to detect debris, Savastano et
al. [2021] highlighted the importance of using a reliable ground truth dataset
within AI training. They recommended researchers attempt to strengthen the
ground data by focusing on the use of in-situ validated ground truth data in a
diverse range of locations.

The authors de Vries et al. [2021] found that their method cannot currently
fill the gap of debris from 50cm down to 5cm, due to the size being too small
to be detected. As most plastics continue to degrade over time, they recommend
future work in this area. Liu and Zhou [2023] also highlighted the importance
of small object detection; they suggest improving network structure or even
exploring alternative techniques.

Within macroplastics there is rich literature on the applications of AI but a
microplastics and nanoplastics review study could be explored. It is an
extremely complex challenge and would require an intricate detection system,
with data collected from under a microscope, perhaps the classes could be
separated into colours or material properties.

In addition, the condition of any size debris should be considered, after
proving that brighter and more rigid pieces of plastic perform well; how would
we solve degradation of colour and texture within debris underwater and
exposure to natural elements? Furthermore, it would be interesting to observe
the results of detection within debris partially or fully covered in sediment
or wildlife. Based on the findings from Sánchez-Ferrer et al. [2022], it would
be valuable to explore research that is able to either detect more than one
object within a frame or be able to explore partially hidden objects.

Sánchez-Ferrer et al. [2023] suggested a plethora of future works such as
upgraded synthetic data generation to better replicate real ocean conditions.
They suggest the following considerations: “object integration, scaling
policies, and depth simulation”. Secondly, they recommend domain adaptation to
reduce the need for annotated data. They also suggest exploring the use of
GANs (Generative Adversarial Network) for generating synthetic data, of which
Fulton et al. [2019] also expressed interest in. Finally, based on their
conclusions, they recommend a deeper exploration of the integration of both
real and synthetic data, potentially investigating the optimal percentage of
synthetic data that advances the overall performance of the model.

###  6.2 Monitoring Debris

Various remote sensing applications could be further modified to conduct long
term monitoring systems, which could enable the prevention of debris entering
large water bodies. Additionally, the assessment of temporal dynamics of
floating debris and its interaction with environmental factors could be a
valuable analysis of debris behaviour in water flow and weather conditions. A
study by University of Oxford; Vogt-Vincent et al. [2023] was able to source
the terrestrial and marine origins of debris into the Seychelles islands using
the Lagrangian trajectory - some of the floating plastic was believed to
travel anywhere up to six months to two years. It could therefore be
interesting to employ machine learning methods to track where these items may
have come from; helping to reduce initial improper waste disposal.
Contributions of citizen scientists, as Teng et al. [2022] suggest; could help
reshape estimations and tracking of debris within the ocean. There are
currently methods for recreational and professional divers who complete the
PADI Dive Against Debris course and log any data collection from scuba diving.

###  6.3 Enhanced Techniques

Enhanced techniques in this field are fundamental to addressing the issue of
marine debris as it provides possible insights to how we can apply detection
and tracking methods to real world scenarios.

Watanabe et al. [2019] proposed multiple sensors and data fusion techniques to
revolutionise object detection in marine environments. Hyperspectral sensors
were suggested by Maharjan et al. [2022] to identify plastics more clearly
against other materials when the spectral properties alter from natural wear
and degradation; they also suggest multispectral sensors to detect smaller
debris. In the same year, Sánchez-Ferrer et al. [2022] suggested future
research could explore other types of data such as sonar or lidar to heighten
the detection and recognition of marine litter. Kylili et al. [2020] suggested
the development of an autonomous image acquisition system that could collect
footage of marine debris in real time and Watanabe et al. [2019] have also
recommended using swarm control techniques for drones; underwater vehicles;
cameras and satellite observations as a collective to monitor and detect ocean
debris. They propose the use of reinforcement learning for optimally
controlling multiple robots in this way.

Most of the research papers have not addressed their efforts to incorporate
measures that protect the fragile underwater ecosystems as we attempt to use
new methods for clean ups, as Fulton et al. [2019] mentions “accidental
removal of biological entities such as plants or animals could damage the very
ecosystems we hope to protect”. The authors are particularly concerned with
environmental safety, as their research is based on the application of their
model on robotics. They apply their model to a NVIDIA TM Jetson TX2 which is
small enough for an AUV and produces minimal heat at a minimal electrical
cost. Their team stated their intention to use their Aqua AUV [Sattar et al.,
2008] in the near future for real time object detection.

Despite this promising work, we feel that applying deep learning methods to
automation is still an underexplored area. Hipolito et al. [2021] also briefly
highlight the potential applications of testing and implementing such
integration. Kasparavičiūtė et al. [2019] explore path planning solutions for
plastic object retrieval using genetic programming (a method that allows the
ability to learn for itself). They deployed this onto an AUV which is capable
of diving underwater and used a range of sensors to detect plastic. They do
however, register a multitude of challenges including consideration of
exclusion zones; safety & rollback; realistic path planning; battery
consumption and general enhancement in the system. Fulton et al. [2019] also
express the challenges of automation including “the development of navigation,
exploration and manipulation strategies”.
However, upon conclusion of this review; we believe that in order to begin
applying machine learning to automation for debris collection, researchers
must first establish a much more in depth database and consider the many gaps
in the research for detection and mapping algorithms.

Acuña-Ruz et al. [2018] suggest using the results of their study on marine
debris on beaches, where they combined three algorithms (RF, SVM & LDA) to
create an environmental management strategy for beach cleans and other means
of decision making programs regarding beach debris. They particularly
emphasise further developing AI methods and hyperspectral sensors for the
classification of marine debris using satellite imagery.

Before testing any model in real world scenarios, it could be beneficial to
create realistic simulations representing all classes with an introduction of
new types of debris to monitor how the model would perform and behave.

###  6.4 Deep Learning Methods

The next steps in this research could be reviewing and comparing the strongest
algorithms currently available (Faster R-CNN, YOLOv3, YOLOv4 & YOLOv5) for
object detection with marine debris. This would then have the potential to
develop the best performing model into a hybrid option.

To avoid the natural bias that CNNs may produce; transformers could be
explored further in combination with CNNs; to achieve a strong performance
they would require a large database and powerful computer resources. There is
positive research to suggest strong results with this combination by Bazi et
al. [2021], Carion et al. [2020], Liu et al. [2021] & Touvron et al. [2021]
but minimal research of transformer CNN hybrids in marine debris.

Kikaki et al. [2022] suggested several directions when testing their benchmark
dataset MARIDA. The authors proposed exploring training their dataset on more
advanced machine learning models or investigating the transferability of the
models on the dataset to other regions and sensors. The authors also
highlighted the importance of developing methods for tracking and predicting
marine debris; especially within ocean circulation models so that as
researchers we can all better understand the way marine debris travels.
Sannigrahi et al. [2022] suggest separating the training data and utilising a
multi object detecting model to differentiate between organic floating debris
and floating plastic. However, once again as mentioned above this would
require a larger dataset reflecting more diversity, as well as broadening the
regions where data is obtained.
Zhou et al. [2023] highlights the development of high performance and low size
networks using dilated parallel modules (DPMs) as a promising direction -
particularly in one-stage small object detection networks to transcend network
performance.
Liu and Zhou [2023] suggest exploration of real-time deployment, particularly
of their refined YOLOv5 model; including testing the models on different
hardware applications for practical applicability in real-world scenarios.

It is also worth mentioning that a few researchers including Xue et al.
[2021b], Zhou et al. [2023] and Liu and Zhou [2023] recommend enhancing the
performance of CNNs specifically for marine debris identification such as
exploring different backbones, introducing additional attention mechanisms and
incorporating other techniques to boost model performance.

###  6.5 Evaluation Metrics

Evaluation metrics are used to assess the results of the model, which provides
researchers with a better insight on how well the model is performing.
Sánchez-Ferrer et al. [2022] touch briefly on how evaluating their model using
the mean average precision may not be fully capturing the performance of the
model, therefore, including a diverse array of evaluation metrics with each
model trained could be beneficial. Watanabe et al. [2019] suggest evaluating
the performance quantitatively for various deep network structures on
different hardware options.

Based on the literature review and the results, deep learning for addressing
marine debris has achieved great strides in the last five years, however, in
order to make significant progress, there is a substantial journey ahead for
researchers in this field, with an abundance of novelties to confront.

## Acknowledgements

Declaration of generative AI and AI-assisted technologies in the writing
process During the preparation of this work the author(s) used [ChatGPT-4] in
order to [FIND EFFECTIVE SYNONYMS AND IMPROVE THE WRITING QUALITY]. After
using this tool/service, the author(s) reviewed and edited the content as
needed and take(s) full responsibility for the content of the publication.

## Appendix A

Google Scholar was searched for “deep learning marine debris” OR “deep
learning marine macro debris”. In IEEE we searched for “deep learning marine
debris”.

We have checked references. Papers excluded from this survey paper were either
before 2018 or explored micro or nano debris detection. It is worth also
mentioning there is another database called JeDI “The Jellyfish Database
Initiative” which could be easily confused with JEDI ”JAMSTEC E-library of
Deep-Sea Images”. Some researchers in oceanscapes might use JeDI for tracking
jellyfish populations. [Condon and Lucas, ].

## Supplementary

Table 1 Metadata comparison of all analysed papers from ’Section 3. Deep
learning techniques’ in marine debris identification.

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##  7 Author Statement

#### Zoe Moorton

Conceptualization, Methodology, Formal analysis, Investigation, Resources,
Software, Validation, Data Curation, Writing - Original Draft, Visualization

#### Dr. Zeyneb Kurt

Validation, Writing - Review and Editing, Supervision

#### Dr. Wai Lok Woo

Writing - Review and Editing, Supervision

##  8 Acknowledgments

Once again, I would like to thank the following who made this study happen by
generously volunteering their data.

  * 1.

Maria Oskarsson

  * 2.

Dr. Melanie Bergmann, Alfred Wegener Institute Helmholtz Centre for Polar and
Marine Research

  * 3.

Minna Zakheou, Viking Divers Ltd

  * 4.

Nic Emery, Fifth Point Dive.

  * 5.

Philip Robinson, Ay Nik Community Divers.

  * 6.

Thank you to the diving communities who helped contribute imagery.

I would also like to acknowledge JAMSTEC and Pexels for providing an open-
source database to the public, from which I obtained much of my data. Thank
you to Ya’el Seid-Green, NOAA Marine Debris Program, who once again provided
me with invaluable insight, and to Laura Anthony, Elizabeth Duncan and Heather
Coleman, NOAA Federal for all their help.