# From Blurry to Brilliant Detection: YOLOv5-Based Aerial Object Detection
with Super Resolution

Ragib Amin Nihal1,∗, Benjamin Yen1,3, Katsutoshi Itoyama1,2 and Kazuhiro
Nakadai1,∗ 1Department of Systems and Control Engineering, School of
Engineering, Tokyo Institute of Technology2Honda Research Institute Japan
Co.Ltd.3International Research Fellow of the Japan Society for the Promotion
of Science (JSPS) at the Nakadai Lab, Tokyo Institute of
Technology.∗Corresponding Author, email<EMAIL_ADDRESS>
###### Abstract

The demand for accurate object detection in aerial imagery has surged with the
widespread use of drones and satellite technology. Traditional object
detection models, trained on datasets biased towards large objects, struggle
to perform optimally in aerial scenarios where small, densely clustered
objects are prevalent. To address this challenge, we present an innovative
approach that combines super-resolution and an adapted lightweight YOLOv5
architecture. We employ a range of datasets, including VisDrone-2023,
SeaDroneSee, VEDAI, and NWPU VHR-10, to evaluate our model’s performance. Our
Super Resolved YOLOv5 architecture features Transformer encoder blocks,
allowing the model to capture global context and context information, leading
to improved detection results, especially in high-density, occluded
conditions. This lightweight model not only delivers improved accuracy but
also ensures efficient resource utilization, making it well-suited for real-
time applications. Our experimental results demonstrate the model’s superior
performance in detecting small and densely clustered objects, underlining the
significance of dataset choice and architectural adaptation for this specific
task. In particular, the method achieves 52.5% mAP on VisDrone, exceeding top
prior works. This approach promises to significantly advance object detection
in aerial imagery, contributing to more accurate and reliable results in a
variety of real-world applications.

##  I INTRODUCTION

The contemporary global dependence on Earth observation facilitated by drones,
satellites, and airborne technology has inaugurated a period characterized by
the acquisition of high-resolution imagery of the Earth’s surface. The
potential of this ability is vast and can be used in several sectors, such as
disaster management, precision agriculture, wildlife conservation, and urban
monitoring [1]. The core of these revolutionary applications centers around
the critical undertaking of aerial object detection, which represents a key
obstacle in the field of computer vision. The task involves the accurate and
effective detection of predetermined items inside these images of high
resolution. Nevertheless, this task is filled with obstacles that are specific
to aerial imagery [2], like the presence of different scales, the clustering
of objects in a dense manner, and the existence of intricate geographic
components. Because of these inherent complexity, object recognition in aerial
images is a particularly difficult task.
In recent years, strides in deep learning, particularly data-driven
approaches, have revolutionized segmentation and object detection tasks across
various domains [3]. The accuracy and efficacy of object detection algorithms
critically hinge on the quality and diversity of training datasets. While
benchmark datasets like PASCAL VOC and MS COCO [4] have spurred advancements
in object detection for natural images, these frameworks fall short when
applied to the distinctive domain of aerial and satellite images. Table I
illustrates a noteworthy accuracy disparity between state-of-the-art (SOTA)
object detectors when applied to the COCO and Visdrone2018 datasets. This
divergence is primarily attributable to disparities in the environmental
conditions and object characteristics between these two dataset categories.

TABLE I: Comparative Discrepancy: A comparison of the performance disparity
between select state-of-the-art (SOTA) detectors, namely SSD, YOLOv3,
RefineDet, Faster-RCNN, and RetinaNet, when applied to the MS-COCO [4] and
Visdrone2018 [5] datasets. The accuracy statistics pertaining to the
Visdrone2018 dataset have been sourced from [6].

Dataset | SSD | YOLOv3 | RefineDet | Faster-RCNN | RetinaDet
---|---|---|---|---|---
MS-COCO | 26.81 | 33.03 | 41.79 | 41.48 | 39.13
VisDrone2018 | 2.52 | 20.03 | 21.07 | 21.34 | 31.88

In the cases of COCO [4] and ImageNet [7], the objects present in natural
images are often captured under more favorable conditions. These conditions
typically involve simpler backgrounds, reduced variability in scale, and
larger object sizes, collectively rendering the objects more discernible and
distinguishable. In contrast, the objects contained in aerial image datasets
frequently exhibit substantial variations in illumination, scale, and viewing
angles. They tend to be smaller in size and set against complex and cluttered
backgrounds, significantly challenging the task of capturing robust feature
representations for these smaller objects and, consequently, achieving precise
object detection.

This research takes on the challenge of enhancing object detection in aerial
imagery through a novel method called B2BDet (From Blurry to Brilliant
Detection). It does so through a combination of innovative strategies, making
the following significant contributions:

  * •

Custom Super Resolution Model: Recognizing that off-the-shelf super resolution
models do not meet the unique demands of aerial images, we propose to
introduce a fine tuned super resolution model designed explicitly for the
enhancement of image quality.

  * •

Super Resolved (SR)-YOLOv5 Architecture: A modified YOLOv5 [8] architecture is
employed to maximize detection performance in aerial imagery, integrating with
the super resolution model. This innovative lightweight architecture leverages
CSPDarknet53, PANet, Transformer Prediction Heads (TPH), and Convolutional
Block Attention Modules (CBAM) for optimized feature extraction and precise
object localization. Careful modifications were made to compress the SR-YOLOv5
model while retaining optimal performance.

  * •

Extensive Evaluation on Diverse Aerial Datasets: Our research leverages a
comprehensive collection of aerial image datasets, including satellite images,
drone-captured scenes, and more. This diverse dataset application ensures that
the model’s understanding of aerial object detection is both extensive and
adaptable.

These contributions come together to address the challenges posed by aerial
imagery, offering a state-of-the-art solution for object detection. With a
focus on improved image quality, novel architecture, and versatile dataset
applications, this research promises to make "From Blurry to Brilliant
Detection" a reality in the field of aerial object detection.

##  II LITERATURE REVIEW

###  II-A General object detection

Object detection in aerial images has gained significant attention in recent
years, prompting the development of various CNN-based object detection
techniques [9, 10]. Convolutional neural networks (CNNs) have become the
dominant approach for object detection. Common object detectors can be divided
into one-stage detectors like YOLO [11] and RetinaNet [12] and two-stage
detectors like Faster R-CNN [13]. Recent research has developed detectors
optimized for aerial imagery, but they generally follow the same one-stage or
two-stage framework using a backbone network, neck, and detection head.

  * •

Backbone networks like ResNet and EfficientNet extract features from the input
image. They are commonly initialized with weights pretrained on large datasets
like ImageNet. This transfers generalized visual knowledge before task-
specific fine-tuning.

  * •

Neck modules like FPN and PANet refine the backbone features for detection.
This includes combining different scale features and adding contextual
information.

  * •

The detection Head then uses the neck features to classify and localize
objects. One-stage heads like YOLO predict bounding boxes and classes
directly. Two-stage heads like Faster R-CNN first propose regions of interest
then classify each region.

(a)

(b)

(c)

(d)

Figure 1: (a) is a sample image where we used super resolution. For comparison
we examined the red box areas. (b), (c), (d) are respectfully the original
version, the pretrained model version of ESRGAN, and the our fine tuned
version for aerial imagery. The pretrained model exhibits distortions such as
blurring and loss of detail. Our fine-tuned model produces a sharper, clearer
result by learning the unique visual patterns and artifacts present in aerial
imagery. Fine-tuning enables the model to reconstruct finer details and
textures, better recovering the original resolution from the low-resolution
input.

###  II-B Object Detection on Aerial Images

The study field of computer vision has witnessed a significant rise in
interest in object detection in aerial images [14]. This surge in attention
can be attributed to the growing utilization of drones and other aerial
platforms for various purposes such as surveillance, mapping, and inspection.
Nevertheless, the task of detecting objects in aerial images has many distinct
challenges when compared to images captured at ground level. Researchers have
developed specialized object detectors to handle small objects in aerial
views, such as Deconvolutional Single Shot Detector (DSSD) [15], feedback loss
functions [16], and attention mechanisms [17]. Methods to reduce false
detections induced by cluttered backgrounds include contextual modeling [18],
rotation invariance [19], and improved feature fusion [20]. Object detectors
designed specifically for dense aerial scenes have been proposed, like MSA-
YOLO [21], SCA-YOLO [22]. To improve detection accuracy, recent works have
explored fusing multi-scale feature maps [15, 20] and combining global and
local context [18, 19] to handle varying object scales. Attention mechanisms
are widely used to focus feature extraction on relevant image regions [23].
Existing object detection methods have made significant contributions to the
field, focusing on specific datasets and addressing the challenges in aerial
imagery, such as small object detection, complex backgrounds, and challenging
environmental conditions. However, these methods are often tailored to
particular datasets, limiting their generalizability.

###  II-C Super Resolution Network

Super resolution techniques aim to increase the resolution and enhance image
details. This is critical for improving object detection in aerial images,
where objects tend to appear small and indistinct. However, general super
resolution methods developed for natural images do not transfer well to the
aerial domain. One notable approach is the use of Generative Adversarial
Networks (GANs) for super resolution. GANs, such as SRGAN [24], have shown
great potential in producing realistic and high-quality super-resolved images.
SRGAN employs a generator network to upscale images and a discriminator
network to distinguish between the generated images and real high-resolution
images, thus encouraging the generator to produce more authentic results.
Another popular super-resolution method is ESRGAN [25], which enhances SRGAN
by incorporating a feature extraction network. This addition further improves
the visual quality of super-resolved images. While super-resolution techniques
have been primarily applied to general imagery, their adoption in the domain
of aerial and remote sensing imagery is less explored. While super-resolution
techniques have shown promise, off-the-shelf models still struggle with the
unique challenges of aerial imagery. As shown in Figure 1, directly applying
pretrained super resolution models can result in artifacts like blurring and
distorted textures on aerial images. The unique challenges posed by aerial
images, such as variations in scale, illumination, and environmental
conditions, necessitate tailored solutions.
In this paper, we extend the application of super-resolution techniques to
aerial imagery. By enhancing image quality through super-resolution, we aim to
improve the performance of object detection in aerial images, particularly in
scenarios with small and challenging-to-detect objects. This integration of
super-resolution with custom YOLOv5 architecture represents a novel approach
to address the specific challenges of object detection in aerial contexts.

##  III PROPOSED METHOD – B2BDET

###  III-A Overview

The key objective of this work is to enhance object detection in challenging
aerial imagery containing small, blurred objects against complex backgrounds.
To achieve this goal, a two-stage pipeline (Fig. 2) is proposed. The first
stage applies super resolution as a preprocessing technique. Super resolution
using a GAN framework like SRGAN can enlarge and enhance small, blurry objects
by generating realistic high-resolution images from low-resolution aerial
photos. This is crucial because the small object sizes and lack of clarity due
to distance, motion and atmospheric effects makes detection difficult. The
second stage uses a SR YOLOv5 model for optimized object detection on the
super resolved aerial images. YOLOv5 provides high accuracy while still
allowing real-time performance due to its unified one-stage architecture.
Modifying components like its backbone, neck and detection head enables
adapting YOLOv5 specifically for challenges inherent in aerial imagery, such
as small objects, clustered scenes, and complex backgrounds.

Figure 2: B2BDet: Overview of the proposed two-stage method for aerial object
detection. The first stage applies super resolution using a custom SRGAN model
to enhance the input aerial image resolution and quality. The second stage
performs object detection on the super resolved image using a tailored YOLOv5
model.

###  III-B Super Resolution

To improve the quality of aerial images for object detection, we develop a
custom SRGAN tailored to aerial imagery. The overall architecture follows the
SRGAN [24] framework consisting of a generator model for upscaling images and
a discriminator model for differentiating real and generated images (Fig 3).
The generator model contains residual blocks with convolutional layers to
upscale input images. By competing against the generator, the discriminator
provides adversarial feedback that helps produce sharper and more realistic
High Resolution images. The SRGAN model is trained on diverse aerial image
datasets consisting of satellite, drone, and airborne photos. Using extensive
data covering various conditions ensures the model generalizes well to
different aerial images for improved super-resolution performance.

Figure 3: SRGAN based on [24]: The input picture resolution is increased by
two trained sub-pixel convolution layers in the deep generator network, which
contains B residual blocks with the same architecture. We train a
discriminator network to separate produced SR samples from actual HR photos.
Each convolutional layer’s kernel size (k), number of feature mappings (n),
and stride (s) are provided. The model is trained from scratch on aerial image
datasets to learn mappings suitable for this domain.

By tailoring the SRGAN architecture and training for aerial images, we are
able to super-resolve input images as a preprocessing step to significantly
enhance their quality before object detection.

###  III-C SR-YOLOv5: Super Resolved YOLOv5

YOLOv5 was chosen for aerial imaging due to its unified one-stage
architecture, state-of-the-art performance, customizable key components, and
strong baseline. It offers fast, efficient object classification and bounding
box regression, maintains high FPS, and allows domain-specific optimizations,
reducing training time. Our custom YOLOv5 architecture, named SR-YOLOv5 for
its integration with the super resolution model, is designed to address the
specific challenges associated with detecting objects in aerial images. It
combines the efficiency of the YOLO framework with modifications that improve
object detection accuracy and robustness in the context of aerial imagery
(Figure 4).
Backbone: The backbone utilizes a reduced CSPDarknet53 model to efficiently
extract features from input images. A key modification is the integration of
C3STR (3 convolutions with Swin Transformer) [17] modules, which replace some
standard C3 modules in the backbone. The transformer encoder block structure
is motivated by the vision transformer. It is designed to capture global
information and rich contextual details. Compared to the original bottleneck
block, the transformer encoder block has the potential to capture global
information with the help of a self-attention mechanism, which is crucial in
aerial images. The architecture starts with a "Focus" module, which performs
an initial convolution operation. This helps capture low-level features
efficiently. This initial Focus layer is vital for reducing computational
costs and enhancing the model’s ability to detect small objects.
Neck: The architecture employs a Feature Pyramid Network (FPN) head as the
neck module. The FPN head is responsible for combining features from different
levels of the backbone using operations like upsampling and concatenation.
This multi-scale feature aggregation mechanism enables the model to utilize
information from both lower and higher resolution feature maps. This proves
crucial for detecting objects across varying scales, which is common in aerial
imagery. The FPN neck allows the model to leverage both semantically strong
features to detect larger objects as well as spatially detailed features to
localize smaller objects.
Head: The detection head incorporates modules to expand the model’s depth and
receptive field. Spatial Pyramid Pooling (SPP) is utilized to provide a
spatial pyramid pooling effect, enhancing the model’s capacity to gather
features across multiple scales within a single receptive field. BottleneckCSP
modules further contribute to increasing the overall depth and complexity of
the model. Together, these components improve the model’s ability to
comprehend intricate patterns and represent complex features found in dense
aerial imagery.

Figure 4: SR-YOLOv5: Our custom detector architecture

###  III-D Training

During the SRGAN training, The model is trained on diverse aerial image
datasets consisting of satellite, drone, and airborne photos using an
adversarial loss between the generator and discriminator. After getting the
super resolution images, the object detection model is trained using
strategies like strong regularization and compound scaling to find the optimal
width and depth scaling factors. Data augmentation techniques mimicking the
complexity of aerial scenes are used, along with tailored anchor boxes
designed to handle small objects based on the training datasets. These
training procedures optimize the model for robust performance on the unique
challenges presented by aerial imagery.

##  IV EXPERIMENTS

###  IV-A Dataset Description

To comprehensively evaluate the proposed B2BDet model, we utilize a diverse
collection of aerial datasets capturing images from various aerial platforms
and environments. Validation on multiple datasets is crucial to ensure the
generalizability and robustness of our approach. Each dataset provides unique
challenges such as small dense objects, complex backgrounds, and different
illumination conditions. Together they enable thorough assessment of B2BDet’s
capabilities in real-world scenarios. The following sections describe these
datasets and their key characteristics that are leveraged for model
validation.

TABLE II: Object detection performance on various methods and backbones on VisDrone Dataset. Method | Backbone | PED | PER | BC | Car | Van | Truck | TRI | ATRI | Bus | MO | mAP50 (%)
---|---|---|---|---|---|---|---|---|---|---|---|---
Fast R-CNN | VGG-16 | 21.4 | 15.6 | 6.7 | 51.7 | 29.5 | 19.0 | 13.1 | 7.7 | 31.4 | 20.7 | 21.7
Faster R-CNN | ResNet101 | 20.9 | 14.8 | 7.3 | 51.0 | 29.7 | 19.5 | 14.0 | 8.8 | 30.5 | 21.2 | 21.8
Cascade R-CNN | ResNet50 | 22.2 | 14.8 | 7.6 | 54.6 | 31.5 | 21.6 | 14.8 | 8.6 | 34.9 | 21.4 | 23.2
RetinaNet | ResNet50 | 13.0 | 7.9 | 1.4 | 45.5 | 19.9 | 11.5 | 6.3 | 4.2 | 17.8 | 11.8 | 13.9
CenterNet | ResNet50 | 22.6 | 20.6 | 14.6 | 59.7 | 24.0 | 21.3 | 20.1 | 17.4 | 37.9 | 23.7 | 26.2
YOLOv4 | CSPDarknet53 | 24.8 | 12.6 | 8.6 | 64.3 | 22.4 | 22.7 | 11.4 | 7.6 | 44.3 | 21.7 | 30.7
DMNet | ResNet101 | 28.5 | 20.4 | 15.9 | 56.8 | 37.9 | 30.1 | 22.6 | 14.0 | 47.1 | 29.2 | 30.3
HRDet+ | HRNetV2-W48 | 28.6 | 14.5 | 11.7 | 49.4 | 37.1 | 35.2 | 28.8 | 21.9 | 43.3 | 23.5 | 28.0
CDNet | CSPDarknet53 | 35.6 | 19.2 | 13.8 | 55.8 | 42.1 | 38.2 | 33.0 | 25.4 | 49.5 | 29.3 | 34.2
HR-Cascade++ | ResNet101 | 32.6 | 17.3 | 11.1 | 54.7 | 42.4 | 35.3 | 32.7 | 24.1 | 46.5 | 28.2 | 32.5
MSC-CenterNet | ResNet50 | 33.7 | 15.2 | 12.1 | 55.2 | 40.5 | 34.1 | 29.2 | 21.6 | 42.2 | 27.5 | 31.1
YOLOv3-LITE | DarkNet-53 | 34.5 | 23.4 | 7.9 | 70.8 | 31.3 | 21.9 | 15.3 | 6.2 | 40.9 | 32.7 | 28.5
MSA-YOLO | CSPDarknet53 | 33.4 | 17.3 | 11.2 | 76.8 | 41.5 | 41.4 | 14.8 | 18.4 | 60.9 | 31.0 | 34.7
SCA-YOLO | CSPDarknet53 | 57.3 | 43.9 | 23.7 | 85.5 | 49.2 | 45.4 | 35.0 | 18.3 | 61.8 | 54.2 | 47.4
B2BDet(Proposed) | CSPDarknet53 | 55.3 | 36.6 | 27.5 | 87.8 | 57.0 | 64.1 | 37.7 | 31.7 | 73.0 | 54.1 | 52.5

VisDrone: Our primary dataset of choice is the VisDrone-2023 dataset, which
contains over 6,000 aerial images captured by drones. It has a total of
343,205 annotated object instances across 11 classes such as cars, buses,
bicycles, and pedestrians. A key characteristic of VisDrone-2020 is that it is
dominated by small objects, with over 87% of instances occupying less than
0.3% of image area. This sets it apart from conventional datasets like COCO
and ImageNet that are biased towards large objects. The large number of small
objects densely clustered together poses a unique challenge for object
detection. Overall, VisDrone-2023’s focus on small objects makes it suitable
for evaluating models on the critical task of small object detection in dense
aerial scenes. The images cover diverse environments and conditions, testing
generalizability of approaches. VisDrone-2023 is the same dataset as
VisDrone-2018 [5].
SeaDroneSee: The SeaDroneSee dataset [26] contains over 2,900 aerial images
for object detection in maritime environments. Images were captured by drones
and fully annotated with 5 object classes essential for search and rescue
operations: swimmers, boats, jet skis, life-saving appliances, and buoys. With
its extensive labeled data of small, visually-similar objects in oceanic
conditions, SeaDroneSee supports developing precise computer vision models for
detecting key objects from aerial drones to assist in critical search and
rescue activities at seas.
VEDAI: The Vehicle Detection in Aerial Imagery (VEDAI) dataset [27] contains
high-resolution aerial images annotated for vehicle detection and
classification. It includes 1,433 aerial images, containing more than 12,608
annotated objects, which are 8 classes of vehicles like cars, trucks, vans,
boats, and tractors captured from aerial platforms under various environmental
conditions. It provides a realistic benchmark for vehicle-focused computer
vision models designed for overhead imagery.

(a)

(b)

(c)

Figure 5: Performance in (a) SeaDroneSee, (b) VEDAI, (c) NWPU-VHR10 dataset.

NWPU VHR-10: It is a publicly available 10-class geospatial object detection
dataset [28] used for research purposes only. The dataset consists of 800
images, 650 of which contain at least one target object and 150 of which do
not contain any targets. The images have a spatial resolution of 0.5 to 2
meters and were acquired from Google Earth and Vaihingen data. The ten classes
of objects in the dataset are airplane, ship, storage tank, baseball diamond,
tennis court, basketball court, ground track field, harbor, bridge, and car.

###  IV-B Implementation Details

The super resolution model was trained on an NVIDIA A100 GPU using a batch
size of 16. The model was fine-tuned for 4000 iterations with an ADAM
optimizer (learning rate=1e-4). For object detection, we used a YOLOv5 model
trained from scratch. Key hyperparameters include a momentum of 0.843,
learning rate schedule from 0.0032 to 0.12, and a weight decay of 0.00036.
Data augmentation techniques like mosaic (1.0) and mixup (0.2) were used. The
model was trained for 100 epochs with a cosine learning rate schedule and
early stopping. Multi-scale evaluation was used, with input sizes from 3072 to
1408 pixels. Mean average precision (mAP) [29] was the primary evaluation
metric for object detection performance. mAP provides a comprehensive measure
of precision and recall across IoU thresholds and object classes.

###  IV-C Results on VisDrone

On VisDrone, our method achieves 52.5% mAP exceeding state-of-the-art
techniques like SCA-YOLO (47.4% mAP) and YOLOv4 (30.7% mAP). The significant
boost demonstrates the benefits of super resolution and YOLOv5 optimizations
for congested small object detection. Table II presents the performance across
all the classes in terms of average precision. Other results are collected
from [22].

###  IV-D Results on Other Datasets

On NWPU-VHR10, consisting of very high resolution aerial images, our approach
attains a 90.5% overall mAP across 10 classes including airplanes, ships, and
vehicles. In particular, it scores 99.5% and 96.9% mAP on the airplane and
vehicle categories respectively, demonstrating its robustness.
Experiments on the SeaDroneSee maritime search and rescue dataset also show
strong results of 76% overall mAP. Despite the challenge of distinguishing
between visually similar classes like swimmers, boats, and buoys, the method
achieves 96.3% and 93.4% mAP on the boat and jetski categories, validating its
efficacy for critical detection tasks.
Finally, on the vehicle-focused VEDAI dataset, the proposed technique secures
77.5% overall mAP, with especially high mAP of 89.3% on cars. This confirms
its capability to precisely categorize vehicles in aerial views across varied
environments.
Extensive experiments were conducted to evaluate the proposed approach on
major aerial image datasets including VisDrone, NWPU-VHR10, SeaDroneSee, and
VEDAI. The results demonstrate superior performance over existing methods and
validate the effectiveness of the innovations presented in this work.

###  IV-E Efficiency of SR YOLOv5 Detector

While the proposed approach employs a two-stage pipeline with super resolution
followed by object detection, optimizations were made to ensure the custom
YOLOv5 model remains lightweight and efficient. Table III compares the number
of layers, parameters, and computational cost of our SR YOLOv5 detector
against other YOLOv5 variants and recent methods. Compared to other recent
methods like TPH-YOLOv5, our model maintains a smaller footprint. This
demonstrates how domain-specific customization enables compact, efficient
models without sacrificing accuracy. Our experiments focused on balancing
detection performance with lightweight design suitable for real-time drone and
aerial applications.

TABLE III: Comparison of Model Layers and Parameters Model | Layers | Parameters (M) | FLOPs (B)
---|---|---|---
YOLOv5-L | 335 | 47.0 | 115.4
YOLOv5-X | 407 | 87.7 | 218.8
TPH-YOLOv5 | 470 | 60 | 207.0
B2BDet(Proposed) | 270 | 27.7 | 109.5

##  V DISCUSSION

###  V-A Correlation with number of training instances

There appears to be some correlation between the number of training instances
for each object class and the detection performance on that class. For
example, on VisDrone the car category with over 200,000 instances secures a
high 87.8% AP. In contrast, the awning tricycle category with only 4,000
instances achieves lower 31.7% AP. More training data for a class enables the
model to better learn and detect those objects.

###  V-B Unlabeled objects

While quantitative results demonstrate clear improvements from our method,
they underestimate the full capabilities of B2BDet. A key limitation we
encountered stems from incomplete or inconsistent annotation of test images.
As illustrated in Figure 6, qualitative examination reveals that the model can
in fact detect small and obscured objects that are visibly present but not
annotated in the test data. However, our B2BDet model successfully detects
many of these unannotated objects, indicating its strong generalization
capabilities. The presence of unlabeled objects negatively biases the
quantitative evaluation, since they are counted as false positives despite
being valid detections. This suppresses reported performance on metrics like
mAP which rely on the annotated ground truth being exhaustive. In essence, the
model is penalized for exceeding the capabilities of the human annotation
effort. We performed extensive qualitative analysis and found many similar
cases where lack of annotations failed to account for all discernible objects.
The degree of under-annotation varies across datasets and categories,
impacting certain classes more than others. Vehicle and people classes tend to
be most affected, as obscured and distant, easily missed in manual labeling.
While annotation quality and coverage affect all models, this issue
disproportionately hinders methods with higher generalization like B2BDet.
Models that rely more on memorization cannot detect unlabeled instances.
Enhanced generalization thus exposes the shortcomings of annotations. This
represents an important direction for improvement in data preparation.
Expanding the scope and consistency of labeling to match modern model
capabilities would provide fairer assessment and prevent under-estimating
performance. In the interim, we recommend evaluating on both annotated and
unannotated test images to fully gauge model strengths. Qualitative human
review on a sample of predictions can help estimate the prevalence of
unlabeled objects and compensate when interpreting quantitative results. With
these prudent analysis practices, major gains in detection performance can be
unlocked without being obscured by evaluation limitations.

Figure 6: Example in VisDrone dataset where ground truth annotations (left)
fail to label certain objects, while our model (right) successfully detects
them.

###  V-C Limitations

While results are promising, there remain limitations and opportunities for
further improvement. The model still struggles with extremely small,
particularly low-resolution objects under 10 pixels. Occluded objects in dense
clusters are another challenge during inference. On boarding cameras with
lower image quality, noise and distortion negatively impact detections. Future
work should expand testing across more aerial platforms and conditions to
uncover additional limitations. Another side is, the model detects objects
independently but lacks explicit modeling of inter-object occlusion patterns
and relationships. Incorporating contextual information could aid in resolving
clustered, overlapping instances.

###  V-D Thinking Beyond Incremental Improvements

While this work focuses on advancing aerial object detection, it is worth
reflecting on the overall direction of the field. Human perception involves
holistic scene understanding rather than isolating objects. Should computer
vision aim to mimic such cognition versus pursuing incremental metric gains?
Exploring new architectures and objectives for unified scene representation
could complement ongoing improvements in labeling individual objects.
Representing overall scene gist, context, configurations and dynamics may
unlock new capabilities beyond exhaustive classification. By incorporating
higher-level relationships, physics, conceptual knowledge, and other facets
nearer to human reasoning, models can potentially expand from passive
recognizers toward more versatile cognitive systems. While out of scope here,
reflecting on these alternative futures and the role of interpretable
decisions can guide innovation in comprehensive directions. A diversity of
paradigms will likely advance the field collectively.

##  VI CONCLUSION

This paper presented a new aerial object detection method using super
resolution and optimized YOLOv5. Extensive experiments validate the
effectiveness of the approach for detecting small, dense objects in
challenging aerial imagery. The key innovations include: A custom SRGAN
tailored for aerial images to enhance input quality, a modified YOLOv5
architecture optimized for aerial object detection, and a two-stage pipeline
applying super resolution preprocessing before detection. The results
demonstrate state-of-the-art performance on major datasets like VisDrone,
NWPU-VHR10, SeaDroneSee, and VEDAI. In particular, the method achieves 52.5%
mAP on VisDrone, exceeding top prior works. This work advances aerial object
detection research by addressing limitations of existing methods on small,
blurred, clustered object scenarios. The integrated solution synergistically
leverages super resolution and tailored YOLOv5 to push boundaries of detection
accuracy in dense aerial imagery. Future work can expand the variety of
datasets for more robust generalization. Additional robustness to motion blur,
low image quality, and extreme occlusion can be pursued. Investigation into
real-time optimization and efficient deployment on aerial platforms provides
another valuable direction. By building on the techniques presented, further
advances in real-world aerial object detection can be achieved.

## ACKNOWLEDGMENT

This work was supported by JSPS KAKENHI Grant No. JP19KK0260, JP20H00475, and
JP23K11160.

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