# Vision-Guided Active Tactile Perception for Crack Detection and
Reconstruction

Jiaqi Jiang, Guanqun Cao, Daniel Fernandes Gomes and Shan Luo All the authors
are with the Department of Computer Science, University of Liverpool,
Liverpool L69 3BX, U.K. Emails:{jiaqi.jiang, g.cao, danfergo,
<EMAIL_ADDRESS>
###### Abstract

Crack detection is of great significance for monitoring the integrity and
well-being of the infrastructure such as bridges and underground pipelines,
which are harsh environments for people to access. In recent years, computer
vision techniques have been applied in detecting cracks in concrete
structures. However, they suffer from variances in light conditions and
shadows, lacking robustness and resulting in many false positives. To address
the uncertainty in vision, human inspectors actively touch the surface of the
structures, guided by vision, which has not been explored in autonomous crack
detection. In this paper, we propose a novel approach to detect and
reconstruct cracks in concrete structures using vision-guided active tactile
perception. Given an RGB-D image of a structure, the rough profile of the
crack in the structure surface will first be segmented with a fine-tuned Deep
Convolutional Neural Networks, and a set of contact points are generated to
guide the collection of tactile images by a camera-based optical tactile
sensor. When contacts are made, a pixel-wise mask of the crack can be obtained
from the tactile images and therefore the profile of the crack can be refined
by aligning the RGB-D image and the tactile images. Extensive experiment
results have shown that the proposed method improves the effectiveness and
robustness of crack detection and reconstruction significantly, compared to
crack detection with vision only, and has the potential to enable robots to
help humans with the inspection and repair of the concrete infrastructure.

##  I INTRODUCTION

Cracks in the infrastructures surface are important indicators for assessing
the condition of buildings and need to be repaired timely for preventing the
expansion of potential risks. However, manual crack detection and maintenance
are not only time-consuming and expensive, but also pose health risks to human
workers in harsh and complex environments such as dams and underground
pipelines. Hence, development of an effective and robust crack detection
system will be significant for the substitution of inspection workers and the
development of smart cities [1].

During the past two decades, the problem of crack detection attracts wide
attention from researchers and has been widely explored and developed [2].
However, most of the previous works address this task based on only one single
modality such as acoustic data [3] [4], laser-scanned image [5], optical fiber
signal [6], and RGB images [7]. In this case, those methods using the high-
precision modality such as acoustic data cannot perceive objects quickly due
to the limitation of the small receptive field. Moreover, they could not
correct the detection results with additional information if a crack is not
detected or a noisy pattern is falsely detected as a crack.

When humans intend to perceive objects, they gather information and draw a
solid conclusion with their eyes, ears and hands together. In crack detection
tasks, skilled inspectors usually first look at the surface to find areas with
similar color or shape characteristics to cracks, and then use their fingers
or specific tools such as a hammer or ultrasonic device to further inspect
those areas instead of traversing all regions. In this way, they can fully
utilize the properties of different modalities including the efficiency of
visual sense with a large receptive field and the accurateness of tactile
sense or acoustic sense.

Inspired by those observations, we propose a novel vision-guided active
tactile perception approach for crack detection and reconstruction in this
paper. This method integrates visual and tactile senses so that they aid each
other and improve the perception on cracks. On the one hand, visual sense with
a large receptive field can achieve a quick search for finding the candidate
regions containing cracks, which can reduce the area of tactile perception and
improve its effectiveness. On the other hand, tactile sense can enhance the
robustness of crack detection thanks to the characteristics of being less
susceptible to light and noise, and can reconstruct the crack shape precisely
with high-resolution tactile images obtained by a camera based optical tactile
sensor.

To evaluate our proposed method, we collected a visual dataset and a tactile
dataset with 10 mock-up structures that were manufactured using 3D printing.
Half of those objects only contain real cracks, and the rest contain both real
cracks and fake cracks that are paintings on the surface. The extensive
experiment results demonstrate that our approach is capable of distinguishing
real cracks from fake cracks, and reconstructing the crack shape quickly and
accurately. Compared to the visual method, our approach achieves a significant
reduction of mean distance error from 0.82mm to 0.24mm. Furthermore, the
proposed method is more than 10 times faster than passive tactile perception
in terms of tactile data collection time.

Our contributions can be summarised as follows:

  * •

A novel vision-guided active tactile perception framework is proposed, for the
first time, for crack detection and reconstruction;

  * •

A touch point generation method is designed for the vision-guided active
tactile perception;

  * •

A tactile crack perception method is developed that can suppress the false
positives of visual detection and reconstruct the crack with a millimeter-
level resolution.

##  II Related Works

###  II-A Vision-Based Crack Detection

Recently the study of image-based crack detection has drawn increasing
attention from researchers in the fields of both Computer Vision and Civil
Engineering. Early vision-based cracks detection methods mainly rely on
conventional image processing methods such as edge detection [8] [9] and
thresholding [10] [11]. However, those methods are susceptible to light
changes and scanning noises due to the subjective parameter selections for
pre-processing and edge detection. Moreover, there are also works using
conventional machine learning approaches such as Support Vector Machine [12]
[13], Ada-Boost [14] and Naïve Bayes classifier [15]. Nonetheless, the
features used in these traditional machine learning methods such as LBP,
wavelet and histogram can only represent one or two layers of abstraction,
which are unable to capture features of cracks in surfaces with complex
backgrounds.

In the recent years, works using deep neural networks for crack detection have
emerged [16, 17]. Based on the fineness of detection results, those deep
learning-based methods can be summarised as two types of approaches: box-level
detection and pixel-wise detection. Typical box-level crack detection
approaches firstly generate a set of rectangular frames to locate the cracks
using patch scanning [16] or region-based CNN networks [7] and then filter out
false positives with a Naïve Bayes classifier [16] or a Bayesian integration
algorithm [7]. However, the rectangle shape results are not precise and are
limited with regard to complex crack distribution and irregular infrastructure
surface. The more recent development in deep learning-based defects perception
is pixel-wise crack detection. In [18], a novel encoder-decoder network is
proposed, which uses hierarchical convolutional feature learning to separate
the cracks from background. In [19], a deep generative adversarial network is
proposed for pavement crack detection, which is trained end-to-end with a
crack-patch-only supervised method to overcome the local minimum problem.

###  II-B Active Tactile Perception

Tactile sensing plays an important role in robot perception and has been
applied to a number of different tasks such as object recognition [20],
material property analysis [21], and shape exploration [22]. Most early
studies related to tactile perception are based on passive data acquisition
processes and are considered simply as a forward process [21, 23].
Nonetheless, humans usually plan their actions with a goal and actively refine
their sensations (especially the tactile sensation) rather than passively
gather information. Inspired by this, there has been growing interest in
active tactile perception [24, 25]. In [26], an active tactile perception
method using pre-specified control laws was proposed for clothing material
recognition.

However, there has been no works on crack detection with active tactile
perception yet. In [6], tactile sensing was explored for crack detection and
characterization, but in this work tactile data was collected passively and
the position of the crack cannot be localized accurately. Our proposed vision-
guided active tactile perception is the first work that incorporates visual
information into active tactile perception for crack detection and
reconstruction. It can actively perceive the crack region with the guidance of
vision, and reconstruct crack shape precisely with a high-resolution camera-
based tactile sensor.

##  III Methodology

In this work, we propose a vision-guided active tactile perception framework
that can detect and reconstruct cracks efficiently and precisely, with an
overview of the framework illustrated in Fig. 1. Given a single RGB-D image of
cracks in a structure, our method first uses the color image as input to a
deep convolutional network to predict a mask of the cracks. Then the predicted
mask and the depth image are used to generate a set of contact points, which
directs the collection of the tactile images with a camera-based tactile
sensor. Another deep convolutional network is applied to predict the masks of
cracks in the collected tactile images that are used to refine the visual
detection result and reconstruct an accurate cracks profile.

Figure 1: An overview of our vision-guided active tactile crack detection and
reconstruction method. Top row (from left to right): The Deeplabv3+ model is
used to segment the cracks in the visual image. Given the visual crack mask
and the depth image, a set of contact points are generated to guide the
collection of tactile images. Bottom row (from right to left): Another deep
convolutional network is used to segment the crack in the collected tactile
images. Given the detected tactile crack mask, the crack shape are
reconstructed based on the geometrical model of the GelSight sensor and the
coordinate transforming relation between the tactile sensor coordinate and the
world coordinate.

###  III-A Visual Guidance for Touch

A deep semantic segmentation network is first utilized to predict pixel-wise
masks of cracks. To guide the touch sensing, the contact points with cracks
skeletons are then generated. Details of these two steps are given below.

Visual Crack Segmentation. The visual crack segmentation is treated as a
semantic segmentation problem that predicts each pixel of the input image into
one of two semantic classes: (a) background (b) cracks. To this end, we use
the Deeplabv3+ model [27] to segment the cracks in visual images that is a
state-of-art deep learning model for semantic image segmentation. A ResNet-101
backbone [28] is used. Since the number of pixels of the background is much
larger than that of the cracks, the network may easily converge to the status
that treats all the pixels as background. To address this issue, we use the
original images as input instead of resizing it to a smaller size as done in
the previous works [17] and use a weighted cross-entropy loss with crack
pixels weighted 10x more than background pixels. Moreover, we set the output
stride value as 8 as smaller values give finer details in the output mask. In
the training process, we start with a model pre-trained for semantic
segmentation on the COCO dataset [29] and use the following hyperparameters:
SGD Optimizer with constant learning late of 1e-6, momentum 0.9 and weight
decay 5e-4.

Contact Points Generation. Given the predicted pixel-wise crack mask in the
color image, we can extract the skeleton of each crack mask with pattern
thinning method [30]. We define two types of keypoints (i.e., end points and
branch points) and minimal edges that represent the topology of the crack
pattern:

  * •

End points: if they have less than two neighbors.

  * •

Branch points: if they have more than two neighbors.

  * •

Minimal edge $E_{ij}$: if there is a continuous path between two keypoints
$p_{i}$ and $p_{j}$ and all points on the path are neither end points nor
branch points.

For every minimal edge $E_{ij}$ which consists of a number of ordered points,
the keypoint $p_{i}$ is initially selected as the current contact point
$p_{current}$. Then we iteratively choose the next contact point $p_{k}$ using
the following formula:

$\begin{array}[]{l}\max\limits_{k}D[p_{current},p_{k}]\\\ \text{ s.t.
}D[p_{current},p_{k}]<d\end{array}$ (1)

where $D(p_{current},p_{k})$ is the distance between two points in world
frame. The hyper-parameter $d$ is the threshold of the distance between two
points that is related to the coverage and speed of tactile perception. A
smaller $d$ will increase the coverage while reducing the perception speed. In
our case, $d$ is empirically set to four fifths of the tactile sensor’s view
length. As shown in Fig. 1, the end-point pixels and the generated contact
points are tagged with red dots and green dots, respectively. For each contact
points $p_{i}$, the yaw angle of the end-effector is parallel to the vector
$<p_{i},p_{n}>$, where $p_{n}$ is the nearest contact point to $p_{i}$, so
that the end-effector can contact the surface perpendicularly.

###  III-B Active Tactile Crack Perception

Tactile Crack Detection. To address the problem of false positives in the
visual crack detection due to light changes and shadows, we apply tactile
information to refine the vision-based detection results and reconstruct
cracks shape in 3D space. Firstly, we control the robotic arm equipped with a
camera based Gelsight tactile sensor [26] to collect tactile images
autonomously at the generated contact points in Section III-A.

The GelSight sensor is a camera-based optical tactile sensor that can capture
fine details of the object surface. As shown in Fig. 2, a webcam111We use
“webcam” to refer to the camera used in the optical tactile sensor to differ
it from the visual camera used for the vision modality. is placed under an
elastomer and captures the deformations of the elastomer when it interacts
with the object. The sensor has a flat surface and a view range of $14mm\times
10.5mm$ and can capture tactile images at a frequency of 30 Hz [31].

Figure 2: The geometrical model of the GelSight sensor. The webcam at the
bottom captures the deformations of the elastomer and the LEDs project light
to illuminate the space.

The collected tactile images are fed to another deep convolutional network
(Deeplabv3+ [27] with ResNet-101 [28]). Since the number of background pixels
is similar to that of the background pixels in tactile images, we use the
vanilla cross-entropy loss instead of the weighted cross-entropy loss in
Section III-A. Using the predicted masks in tactile images, we can double
check the visual segmentation results. Any minimal edge will be set as false
positives and deleted from visual segmentation results, if there are more than
two tactile images whose predicted crack area is smaller than a predefined
threshold. The threshold is empirically set as one fiftieth of the total
number of pixels in the tactile image.

Tactile Crack Reconstruction. In the task of crack perception, it is crucial
to obtain the shape and size of the crack when assessing its potential risk to
the building and infrastructure. However, limited by the accuracy of the depth
camera, current vision-based reconstruction methods are not able to
reconstruct small cracks precisely. To this end, we use tactile images
obtained from the GelSight sensor to reconstruct the cracks, whose spatial
resolution is about 20 to 30 microns.

First, we predict the location of cracks on the surface of the GelSight
sensor, given the detected boundaries of pixel-wise masks in the tactile
images. To simplify the problem, we model the webcam using a pinhole camera
model, and treat the surface of GelSight sensor as a flat plane that is
perpendicular to the webcam’s $z$ axis. Hence, the transformation between a
contact point $P=[X_{c},Y_{c},Z_{c}]^{T}$ in the tactile sensor coordinates
(the tactile sensor take the centre of the webcam as the origin) to the pixel
$P^{\prime}=[u,v]^{T}$ in the tactile image coordinates can be calculated:

$Z_{c}\left[\begin{array}[]{l}u\\\ v\\\
1\end{array}\right]=K\left[\begin{array}[]{c}X_{c}\\\ Y_{c}\\\ Z_{c}\\\
1\end{array}\right]$ (2)

where $Z_{c}$ is the distance from the optical center to the elastomer surface
of the tactile sensor. $K$ is the matrix of the intrinsic parameters of the
webcam and can be represented as:

$K=\left[\begin{array}[]{llll}\frac{f}{d_{x}}&0&u_{0}&0\\\
0&\frac{f}{d_{y}}&v_{0}&0\\\ 0&0&1&0\end{array}\right]$ (3)

where $f$ is the focal length of the webcam, $d_{x}$ and $d_{y}$ denote the
pixel size, and $(u_{0},v_{0})$ is the center point of the tactile image.

After obtaining the position $P$ in the tactile sensor coordinates, we can
calculate its position $P_{W}$ in the world frame:

$P_{W}=T_{E}^{W}T_{C}^{E}P$ (4)

where $T_{C}^{E}$ and $T_{E}^{W}$ are the transformation matrix from the
tactile sensor coordinates to the end-effector coordinate system, and from the
end-effector coordinate system to the world coordinate system, respectively.

##  IV Experiment Setup

In this section, we introduce the robot setup used for data collection and
experiments. An overview of our robot setup is shown in Fig. 3.

Figure 3:  a) The UR5 robot setup with GelSight sensor and Realsense D435i; b)
GelSight sensor; c) Realsense D435i.

###  IV-A 3D Printed Structures with Cracks

In this work, we follow part of the data acquisition protocol in [6]. A set of
10 structures with cracks of different widths (holes in the structures) were
manufactured with PLA plastic using an i3 Mega 3D printer from ANYCUBIC. To
test the robustness of the proposed method, we also paint several fake cracks
on the surfaces of the structures. The samples are shown in Fig. 4.

Figure 4: Sample structures used for collecting the visual and tactile
dataset. Top row: printed structures with real cracks (holes). Bottom row:
printed pad with fake cracks (painted black blocks).

###  IV-B Visual Data Collection

We put those 3D-printed structures on a table and took 10 images of each
structure with real cracks that are used for training the visual model. Then
we take 3 images for each object separately for the test. All images were
taken with a RealSense D435i camera.

###  IV-C Tactile Data Collection

The setup of tactile data collection is composed of two parts: a 6-DOF UR5
collaborative robot arm from Universal Robots and a GelSight sensor mounted on
a 3D-printed end-effector. To collect the tactile data autonomously and
repeatedly, we build a data collection software using Robot Operating System
(ROS). The software can control the robot arm to move across the surface of a
structure following the pre-defined initial position, steps, and step length
in x-axis and y-axis. As for each position, we rotate the sensor about the
axis perpendicular to the surface with different angles so that the dataset
can be more generalized. As for each contact, the robot arm will stop moving
and the GelSight records one tactile image when the pressure reaches a
threshold. In this way, we can capture tactile data of good quality and also
avoid the unnecessary protective stops of the robot arm. In total, 544 valid
tactile images were collected and split into training and test sets (370 and
174 for each, respectively), with some samples and their annotations shown in
Fig. 5.

Figure 5: Visualization of the tactile images and their annotations for
different cracks.

##  V Experiment Results

###  V-A Experiments on Network Architectures

To evaluate the accuracy of the tactile crack segmentation, we use both
Intersection over Union (IoU) and True Positive rate (TP). The results of
different models and input image sizes are listed in Table I. Two different
backbones was used for Deeplabv3+: ResNet-101 and DRN-54 (Dilated Residual
Network). Example segmentation results of ResNet-101 and DRN-54 with an input
size of 256$\times$256 are shown in Fig. 6. The results indicate that compared
to Resnet-101, DRN introduces more noise over training and perform worse for
the tactile crack segmentation due to the fact that it has a larger receptive
field.

We also experimented with different input image sizes. The results show that a
smaller input image size leads to better performance, which is surprising as
images of a higher resolution may contain more details. One possible reason is
that a certain amount of noise is generated when resizing the width of the
tactile image from 480 to 512.

Figure 6: Visual comparison of different backbones.  TABLE I: Network
Architecture For Tactile Crack Segmentation.

Backbone | Input Size | IoU | TP
---|---|---|---
DRN | 512 | 0.93 | 95.48
DRN | 256 | 0.93 | 97.32
ResNet-101 | 512 | 0.94 | 96.65
ResNet-101[ours] | 256 | 0.97 | 98.87

###  V-B Crack Detection Results

Similar to the evaluation method used in network architectures experiments, we
evaluate our proposed methods based on standard evaluation metrics of pixel
accuracy (pixAcc) and IoU. The segmentation results of using only visual
information and using both visual and tactile information are summarized in
Table II. The results show that both the pixel accuracy and IoU of visual
semantic segmentation have a significant drop due to the existence of fake
painting cracks from 0.899 to 0.866 and from 0.504 to 0.376, respectively. It
is also shown that after using tactile information to find the fake paintings
and refine the visual detection results, the performance of cracks detection
has been improved effectively. Due to the fact that cracks on the RGB images
only take up fewer pixels compared to background, the pixel accuracy does not
change dramatically as IoU.

TABLE II: Crack Detection Accuracy.

Modalities | Fake Painting | pixAcc | IoU
---|---|---|---
vision | $\times$ | 0.899 | 0.504
vision | ✓ | 0.866 | 0.376
vision-tactile | ✓ | 0.909 | 0.636

###  V-C Reconstruction Results

As for the crack reconstruction task, we use the mean, max and standard
deviation (SD) of the shortest distance between the actual crack shape and the
reconstructed crack location to evaluate the accuracy of our proposed method.
There are four methods used for comparison. The vision method uses point
clouds recovered through visual detection and depth information to represent
cracks. In order to reduce the impact of depth information accuracy on
reconstruction, the aligned vision method projects the point cloud to the
table surface. The passive tactile method collects the tactile images through
traversing the whole 3D printed structure surface.

As can be seen in Table III, our approach shows a significant improvement of
mean distance error from 0.55mm to 0.24mm compared to the aligned vision
method. Two example reconstructed crack profiles are shown in Fig. 7, which
shows that reconstructed crack profiles with tactile data is much closer to
the ground truth compared to the vision-based method. On the other side,
compared to passive tactile perception, our proposed vision-guided tactile
perception is more than 10 times faster than passive tactile perception in
terms of the tactile data collection time without affecting the accuracy much.

Figure 7: Visual comparison of different reconstruction methods. blue, red,
yellow, green curves represent the ground truth of the crack profile, aligned-
vision, passive-tactile and our method for crack reconstruction, respectively.
TABLE III: Reconstruction Accuracy

Method | MeanD(mm) | SD(mm) | MaxD(mm) | time(s)
---|---|---|---|---
vision | 0.82 | 0.92 | 4.87 | 1
aligned-vision | 0.55 | 0.53 | 3.78 | 1
passive-tactile | 0.20 | 0.17 | 0.99 | 400
active-tactile[ours] | 0.24 | 0.16 | 0.82 | 35

##  VI CONCLUSION AND FUTURE WORK

In this paper we introduce a novel vision-guided active tactile perception for
crack detection and reconstruction. The cooperation between those two modality
addresses the false positives in visual detection results. The experiments
show that our proposed method can improve the effectiveness and robustness of
crack detection and reconstruction significantly, compared to when only vision
is used. It has the potential to enable robots to inspect and repair of the
concrete infrastructure. Future works to improve our method can also be
considered, such as cracks detection in curved surfaces, the use of weakly
supervised learning methods to segment cracks and addressing the localization
uncertainty of the robot arm in active tactile exploration.

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