Available via license: CC BY-SA 4.0
Content may be subject to copyright.
1
RDD2022: A multi-national image dataset for
automatic Road Damage Detection
Deeksha Arya1,2,, Hiroya Maeda3, Sanjay Kumar Ghosh1,4, Durga Toshniwal1,5, Yoshihide
Sekimoto2
1Centre for Transportation Systems (CTRANS), Indian Institute of Technology Roorkee, Roorkee, India
2Centre for Spatial Information Science, The University of Tokyo, Tokyo, Japan
3UrbanX Technologies, Inc., Tokyo, Japan
4Department of Civil Engineering, Indian Institute of Technology Roorkee, India
5Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, India
Abstract
The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road
images from six countries, Japan, India, the Czech Republic, Norway, the United States, and
China. The images have been annotated with more than 55,000 instances of road damage.
Four types of road damage, namely longitudinal cracks, transverse cracks, alligator cracks,
and potholes, are captured in the dataset. The annotated dataset is envisioned for
developing deep learning-based methods to detect and classify road damage automatically.
The dataset has been released as a part of the Crowd sensing-based Road Damage Detection
Challenge (CRDDC’2022). The challenge CRDDC’2022 invites researchers from across the
globe to propose solutions for automatic road damage detection in multiple countries. The
municipalities and road agencies may utilize the RDD2022 dataset, and the models trained
using RDD2022 for low-cost automatic monitoring of road conditions. Further, computer
vision and machine learning researchers may use the dataset to benchmark the performance
of different algorithms for other image-based applications of the same type (classification,
object detection, etc.).
Background and Summary
The Road Damage Dataset, RDD2022, is an extended version of the existing RDD2020 [1,2]
dataset. Fig. 1 shows the evolution of the dataset over the years. The corresponding
statistics are compared in Fig. 2. Firstly, the dataset RDD2018 [3] was introduced in 2018,
comprising 9,053 road images with 15,435 road damage instances. The RDD2018 dataset
captured the information on eight types of road damage (Table 1) and was utilized for Road
Damage Detection Challenge in 2018, organized as an IEEE Big Data Cup. Fifty-nine teams
from 14 countries participated in the challenge and proposed multiple solutions for
automatic road damage detection using RDD2018. The participants also suggested some
improvements in the annotation files of the RDD2018 dataset.
Considering these suggestions and the imbalance in the road damage categories captured in
the RDD2018 dataset, its authors introduced the extended version of the dataset,
RDD2019[4]. RDD2019 was prepared by correcting the annotations in the RDD2018 dataset
and augmenting it using the Generative Adversarial Network (GAN). It included 13,133
images with 30989 road damage instances. The expanded dataset helped achieve better
accuracy for road damage detection and classification models.
In 2020, Arya et al. (2020) [5,6] attempted to apply the models trained using RDD2019 to
detect road damage outside Japan. Experiments were performed using the data from India
and the Czech Republic. The outcome revealed that the performance of the models trained
using RDD2019 degraded significantly when applied to roads outside Japan. Consequently,
the authors proposed another dataset, RDD2020. The RDD2020[1] dataset was prepared by
Corresponding author: deeksha@ct.iitr.ac.in
2
combining the RDD2019[4] dataset with newly collected data from India and the Czech
Republic. Along with increasing the number of images and damage instances to form
RDD2020, the types of road damage considered were also updated.
Figure 1: Schematic overview of the study design: Evolution of Road Damage Datasets from
RDD2018 to the proposed dataset RDD2022
Since RDD2018 and RDD2019 captured the data from a single country, the damage
categories defined in the Japanese Road Maintenance and Repair Guidebook 20137 were
used. However, in RDD2020, the involvement of multiple countries required considering
multiple road damage standards. Since the criteria for assessing Road Marking deteriorations
such as Crosswalk or White Line Blur vary significantly across different countries, these
categories were excluded from the RDD2020 dataset [1,5]. Subsequently, the following four
damage categories – Longitudinal Cracks (D00), Transverse Cracks (D10), Alligator Cracks
(D20), and Potholes (D40), were included in the RDD2020 dataset.
The RDD2020 dataset was utilized for organizing the Global Road Damage Detection
Challenge (GRDDC’2020) [8]. The challenge invited researchers across the globe to propose a
single model for monitoring road conditions in the three countries (India, Japan, and the
Czech Republic). One hundred twenty-one teams participated in the challenge and proposed
multiple solutions with varying accuracy and resource requirements. The best performing
model utilized the YOLOv5-based ensemble model and achieved an F1 score of 0.67.
Smartphone
images from
Japan
9,053 images
RDD2018
26,336 images
RDD2019
augmented
with
Smartphone
images from
India and
Czech Republic
RDD2018
augmented
using GAN
13,133 images
RDD2019
RDD2020
47,420 images
RDD2020
augmented
with multi-
source images
from Norway,
United States,
and China
RDD2022
3
Figure 2: Statistical comparison of the road damage datasets from 2018 to 2022
Table 1: Road damage types in the RDD2018 dataset [3] and their definitions
Damage Type
Detail
Class Name
Crack
Linear Crack
Longitudinal
Wheel mark part
D00
Construction joint part
D01
Lateral
Equal interval
D10
Construction joint part
D11
Alligator Crack
Partial pavement, overall
pavement
D20
Other Corruption
Rutting, bump, pothole,
separation
D40
Crosswalk blur
D43
White line blur
D44
Further, the analysis conducted by Arya et al. [6] indicated that adding data from other
countries helps improve the generalizability of models trained for detecting road damage in
any country. This analysis and the tremendous success of the GRDDC’2020 [8] are the
motivation behind introducing the current dataset RDD2022. It aims to solve road damage
detection for a more extensive set of countries, targeting solutions for India, Japan, the
Czech Republic, Norway, China, and the United States. The sample images for the four types
of damage considered in RDD2022 are shown in fig. 3. The country-wise samples are
included in the Data Records section of the manuscript.
RDD2022 is prepared and released as a part of the Crowd sensing-based Road Damage
Detection Challenge (CRDDC’2022 - https://crddc2022.sekilab.global/ ). It is divided into two
sets: train and test. The images in the train set are released with annotations. However, the
images in the test set are for testing the models proposed by the CRDDC participants and
#images #labels D00 D10 D20 D40
RDD2018 9053 6460 2768 742 2541 409
RDD2019 13133 20561 5006 4911 7846 2798
RDD2020* 21041 25046 6592 4446 8381 5627
RDD2022* 38385 55007 26016 11830 10617 6544
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
55000
RDD2018 to RDD2022: Data evolution over the years
*Statistics listed only for the publically available training set of the RDD2020 and RDD2022
D00: Longitudinal Crack
D10: Transverse Crack
D20: Alligator Crack
D40: Potholes
4
hence have been released without annotations. The country-wise distribution of images and
labels for RDD2022 is presented in Fig. 4. The distribution of different labels (damage
categories) in the train set is summarized in Fig. 5.
a.
b.
c.
d.
Figure 3: Sample images for road damage categories considered in the data. a. Longitudinal
Crack (D00), b. Transverse Crack (D10), c. Alligator Crack (D20), d. Pothole (D40).
Figure 4: Data statistics for RDD2022: Country-wise distribution of images and labels
Further details regarding the dataset are provided as follows. For data included from Japan,
India, and the Czech Republic, the readers may refer to the article [6]. A summary of the
locations covered in these countries is included here to comprehensively describe the
heterogeneity and robustness considered in the proposed RDD2022 dataset.
i. Japan: The data is collected from seven local governments in Japan (Ichihara city,
Chiba city, Sumida ward, Nagakute city, Adachi city, Muroran city, and Numazu city).
The municipalities have snowy and urban areas that vary widely from the
perspective of regional characteristics like weather and budgetary constraints.
ii. India: The data from India includes images captured from local roads, state
highways, and national highways, covering the metropolitan (Delhi, Gurugram) as
well as non-metropolitan regions (mainly from the state of Haryana). All these
#train_images #test_images #total_images #train_labels
Japan 10506 2627 13133 16470
India 7706 1959 9665 6831
Czech 2829 709 3538 1745
Norway 8161 2040 10201 11229
US 4805 1200 6005 11014
China_M 1977 500 2477 4650
China_D 2401 0 2401 3068
0
2000
4000
6000
8000
10000
12000
14000
16000
18000 RDD2022: Distribution of images and labels
5
images have been collected from plain areas. Road selection and time of data
collection were decided based on road accessibility, atmospheric conditions, and
traffic volume.
iii. Czech Republic: A substantial portion of road images was collected in Olomouc,
Prague, and Bratislava municipalities and covered a mix of first-class, second-class,
and third-class roads and local roads. A smaller portion of the road image dataset
was collected along D1, D2, and D46 motorways to enhance the resilience of the
targeted model.
iv. Norway: The data from Norway consists of two classes of roads a) Expressways and
b) County Roads (or Low Volume Roads). Both types of road classes are asphalt
pavements. Data collection is done by the Norwegian Public Road Administration
(Statens Vegvesen, SVV) and Inlandet Fylkeskommune (IFK). Images provided by SVV
were collected on European Route E14, connecting the city of Trondheim in Norway
to Sundsvall in Sweden. At the same time, the images from IFK belong to different
county roads within Inllanndet County in Norway. The images were collected
without any control over daytime/light, and all the images are natural without
further processing. Further, the dataset captures diverse backgrounds, including
clear grass fields, snow-covered areas, and conditions after rain. Furthermore,
images with different illuminances, such as high sunlight and overcast weather
resulting in daylight, are considered.
v. United States – The data from the United States consists of Google Street View
images covering multiple locations, including California, Massachusetts, and New
York. The image count for each site is provided in fig. 6.
Figure 5: Damage Category-based data statistics for RDD2022
Japan India Czech Norway US China_M China_D
D00 4049 1555 988 8570 6750 2678 1426
D10 3979 68 399 1730 3295 1096 1263
D20 6199 2021 161 468 834 641 293
D40 2243 3187 197 461 135 235 86
0
1000
2000
3000
4000
5000
6000
7000
8000
9000 Damage Category-based Statistics for RDD2022 (Train set)
D00: Longitudinal Crack
D10: Transverse Crack
D20: Alligator Crack
D40: Potholes
6
Figure 6: Details of the locations covered from the United States in RDD2022 data
vi. China: RDD2022 considers two types of data from China: (a) images captured by
Drones (represented as China_Drone or China_D), and (b) the images captured using
Smartphone-mounted MotorBikes (represented as China_MotorBike or China_M).
The drone images were obtained from Dongji Avenue in Nanjing, China. The
MotorBike images were collected on Jiu long hu campus, Southeast University.
Images with normal light, under a shadow environment, and wet stains are covered.
Asphalt concrete pavement is considered with a few exceptions in all six subsets of
RDD2022. The methods used to capture the images and generate the annotations in
RDD2022 are discussed in the following section. A comparative summary of RDD2022 with its
previous versions is presented in Table 2, showing the quantum of improvement in the
proposed dataset.
1022
942
655
520
480
356
309
242
234
150
145
132
132
132
106
76
57
53
50
47
40
34
25
20
18
17
11
0200 400 600 800 1000 1200
Ventura Blvd California
Victory Blvd California
Vanowen St California
Roscoe Blvd California
Valley Circle Blvd California
Wilshire Blvd California
Santa Monica Blvd California LR
Beacon St Massachusetts
Saticoy St California
Ball Rd California
RandomScout2
El Camino Ave California
Flamingo Road California
Sahara Ave California
Washington California
Desert Inn Rd California
Desert Foothills California
Common Wealth Ave Massachusetts
Atlantic Ave New York
Clark Ave California
Sgt Ed Holcomb Blvd S
Westheimer Rd
Briar Forest Dr
Alabama St California
Richmond Ave
RandomScout1
Mason St
Image Count
RDD2022: Data from United States (Locations covered)
7
Table 2: Comparative summary of the proposed RDD2022 dataset with previous versions
RDD2018[3]
RDD2019[4]
RDD2020[1]
RDD2022
(proposed)
#Images
9,053
13,133
26,336
47,420
Image capturing
device
Smartphones
Smartphones
Smartphones
Smartphones,
High-resolution
Cameras,
Google Street View
images
Vehicle for data
collection
Cars
Cars
Cars
Cars,
Motorbikes,
Drones
Image resolution
600x600
600x600
600x600,
720x720
512x512,
600x600,
720x720,
3650x2044
Road view
captured in the
images
Wide view
(Road surface
and
surroundings
captured
horizontally)
Wide view
Wide view
Wide view,
extra-wide view
(using two cameras
in Norway),
top-down view
#Damage
categories
8
9
4
4
#Labels
15,435
(train + test)
for 8 categories
30,989
(train + test)
for 9
categories
21,041
(Released for
training) for 4
categories
55,007
(Released for
training) for 4
categories
Annotation
method
LabelImg tool
LabelImg tool
LabelImg tool
LabelImg tool and
Computer Vision
Annotation Tool
(CVAT)
#Countries
covered
1
(Japan)
1
(Japan)
3
(Japan, India,
Czech Republic)
6
(Japan, India, Czech
Republic, Norway,
United States,
China)
Geographical
diversity
High
(7
municipalities
with diverse
regional and
weather
characteristics)
Same as
RDD2018
Higher
(internationally
diverse regions
are considered
along with a
local variation
for each
country)
Highest
(international
diversity further
enhanced)
8
Methods
The RDD2022 data has been prepared using two steps: image acquisition and damage
annotation. For each of the six countries specified above, road images are captured and
labelled using software to mark the road damage captured in the images. The details of
these two processes are provided as follows.
Image Acquisition
The images in RDD2022 have been acquired using different methods for different countries.
For India, Japan, and the Czech Republic, smartphone-mounted vehicles (cars) were utilized
to capture road images. The installation setup of the smartphone in the car is shown in fig. 7.
In some cases, the setup with the smartphone mounted on the windshield (inside the car)
was also used. Images of resolution 600x600 are captured for Japan and the Czech Republic.
For India, images are captured at a resolution of 960x720 and later resized to 720x720 to
maintain uniformity with the data from Japan and the Czech Republic.
Figure 7: Sample Installation Setup of the smartphone in the car: Image acquisition for data
from India, Japan, and the Czech Republic
For Norway, instead of smartphones, high-resolution cameras mounted inside the
windshield of a specialized vehicle, ViaPPS, were used for data collection (Fig. 8). ViaPPS
System employs two Basler_Ace2040gc cameras (Fig. 9) with Complementary metal oxide
semiconductor (CMOS) sensor to capture images and then stitches them into one wide view
image of a typical resolution 3650x2044.
In contrast, the data for the United States comprises Google Street View Images (vehicle-
based) of the resolution 640x640. Likewise, for China, two types of image-acquisition
methods are considered. The first method includes a camera mounted on motorbikes
moving at an average speed of 30km/h; the corresponding dataset is referred to as
China_MotorBike or China_M. The second method uses a six-motor UAV manufactured by
DJI (M600 Pro) for pavement image collection, resulting in China_Drone (or China_D) data. A
controllable three-axis gimbal was mounted at the bottom of the UAV to hold the camera
and allow 360-degree rotation for capturing the China_Drone data. The corresponding setup
is shown in fig. 10. The resolution of images for the data from China is 512x512.
It may be noted that the China_Drone data has been included only in the training set of the
proposed RDD2022 data to enhance the heterogeneity. The main goal of RDD2022 data still
9
aligns with that of RDD2020, which focuses on low-cost affordable automatic road damage
detection considering feasible methods for the public.
Figure 8: ViaPPS vehicle used to collect data from Norway
Figure 9: Camera set-up in the ViaPPS vehicle used to collect data from Norway
Figure 10: The drone and camera set-up used to capture China_Drone data included in
RDD2022
10
Annotation
RDD2022 includes annotation for road damage present in the image. The software LabelImg
has been used to annotate the images except for the data from Norway. For Norway,
another software Computer Vision Annotation Tool (CVAT), was utilized. Both the software
packages are open source through public repositories:
https://github.com/heartexlabs/labelImg and https://github.com/opencv/cvat, respectively.
Figure 11: Annotation Pipeline (a) input image, (b) image with bounding boxes, (c) final
annotated image containing bounding boxes and class label (D00 in this case)
Figure 12: Sample Annotation in LabelImg tool.
The annotation pipeline is the same as the one used for RDD2020[1] and is presented in Fig.
11. Sample annotation in the LabelImg tool is shown in Fig. 12. All recognized damage
instances were annotated by enclosing them with bounding boxes and classified by attaching
the proper class label. Class labels and bounding box coordinates, defined by four decimal
numbers (xmin, ymin, xmax, ymax), were stored in the XML format similar to PASCAL VOC
[9]. The sample annotation file is shown in Fig. 13.
11
Figure 13: Sample annotation file: XML file corresponding to annotation performed in Fig. 11
Data Records
The RDD2022 data can be accessed at the GitHub repository:
https://github.com/sekilab/RoadDamageDetector. Figure 14 shows the corresponding
directory structure. It comprises seven folders, described as follows:
1. China_Drone: It contains the data from China collected using Drones. Sample images
are shown in Fig. 15.
2. China_MotorBike: It covers the data from China collected using Motorbike. Fig. 16
shows the sample images.
3. Czech: It includes the Czech Republic data collected using vehicle-mounted
Smartphones. The sample images are shown in Fig. 17.
4. India: It consists of the data from India collected using vehicle-mounted
Smartphones. Fig. 18 shows the sample images.
5. Japan: It includes the data from Japan, collected using vehicle-mounted
Smartphones. Fig. 19 shows the sample images.
6. Norway: It includes the Norway data collected using vehicle-mounted high-
resolution cameras. Fig. 20 shows the sample images.
7. United_States: It contains the United States data collected using Google Street View.
Fig. 21 shows the sample images.
Each folder contains a sub-folder, “train,” which includes images (.jpg) and their annotations
(.xml), as described in the previous section. The corresponding statistics (number of images,
number of labels, etc.) are presented in Fig. 4 and Fig. 5. The sample annotation file (.xml) is
shown in Fig. 13.
Along with the “train,” a sub-folder “test” is also included in each of the folders, except for
China_Drone. The “test” directory contains images (.jpg files) for testing the models trained
12
using “train” data. The annotations for “test” images have not been released. The users may
utilize the leader boards on the CRDDC’2022 website to assess the prediction of their models
for images in “test” data.
Figure 14: The directory structure for the RDD2022 dataset
Figure 15: Sample images from China: Collected using Drones
13
Figure 16: Sample images from China: Collected using MotorBikes
Figure 17: Sample images from the Czech Republic
Figure 18: Sample images from Japan
Figure 19: Sample images from India
Usage Notes
The RDD2022 dataset is prepared following the format of the famous PASCAL Visual Object
Classes (VOC) datasets [9]; hence, many existing methods used in the field of image
processing may be applied to it. The usage of RDD2022 may be considered in the following
two types of scenarios:
(i) Images and Annotations - when users want to use both the images and annotations
included in RDD2022 data: The users may wish to use the RDD2022 data directly (as
it is) or augment it for extended applications. A few applications are listed below.
a. Direct use
i. The RDD2022 data lays down the foundation for smartphone-based
automatic road damage detection and is valuable for municipalities
and road organizations for low-cost inspection of road conditions.
14
ii. The data may be used to develop new deep convolutional neural
network architectures or modify the existing architectures to
improve the network’s performance.
iii. Researchers may utilize the data to train, validate, and test the
algorithms for automatically identifying road damages in six
countries.
iv. The images in RDD2022 have been collected using vehicle-mounted
Smartphones (Cameras in the case of Norway). Thus, the models
trained using the RDD2022 data would be capable of damage
detection for data collected through moving vehicles, providing
support for a quick inspection of a large area.
v. Data challenges can be organized using RDD2022. For instance, the
Crowd Sensing-based Road Damage Detection Challenge
(CRDDC’2022) is based on RDD2022. CRDDC aims to find automatic
road damage detection solutions for the six underlying countries of
RDD2022. Likewise, the Global Road Damage Detection Challenge
(GRDDC'2020), organized as an IEEE Big Data Cup in 2020, utilized
the dataset RDD2020, a part of RDD2022, to assess the solutions
proposed by participants for road damage detection in India, Japan,
and the Czech Republic6,8.
vi. Machine learning researchers may utilize the RDD2022 data to
benchmark the performance of different algorithms targeting similar
applications, such as image classification, object detection, etc.
Figure 20: Sample images from Norway
Figure 21: Sample images from the United States of America
15
b. With Augmentation
i. By adding new images: Users may refer to image acquisition in the
Methods section of the manuscript and may augment the RDD2022
with new images targeting multiple applications such as:
Covering more countries: Adding images from other
countries would help automate road damage detection for
those countries and improve the performance for the
currently considered six countries.
More images for the underlying six countries: Currently, the
RDD2022 dataset captures a massive number of images
compared to other datasets. However, it is still not
exhaustive in capturing all possible conditions
(climatic/geographical) of the underlying six countries. New
images may be collected to represent the heterogeneous
scenarios in these countries better and train more robust
models.
Covering new road types: Currently, the images in RDD2022
mainly capture the flexible type of roads. Application for
rigid pavement may be considered by augmenting RDD2022
with new images.
ii. By adding new annotations (damage categories) – The current
version of the data supports the detection and classification of road
cracks (longitudinal, transverse, and alligator) and potholes. Users
may refer to the annotation pipeline in the Methods section of the
manuscript and may augment the RDD2022 to capture the
information of more damage categories – such as Rutting, Raveling,
etc.
This will widen the scope of application for monitoring road
conditions. The models trained using the augmented
RDD2022 would help provide more specific inputs to the
road agencies for requisite maintenance of the roads
captured in the images.
(ii) Only images: The users may utilize the images from RDD2022 and generate new
annotations targeting new applications. For instance:
a. If the users want to keep the domain of the application the same as
RDD2022, that is, Road Damage Detection - Pixel-wise annotations may be
created for segmentation-based applications.
b. Likewise, the users may annotate the images targeting other domains such
as road asset identification, traffic monitoring, congestion detection, etc.
Here, it may be noted that the vehicles in the Norwegian dataset included in
RDD2022 have been masked in the images. Hence, the vehicle-related
applications using RDD2022 may be targeted for Japan, India, the United
States, the Czech Republic, and China, but not for Norway.
Acknowledgments
We thank the researchers Hiroshi Omata and Takehiro Kashiyama for their support in
organizing the CRDDC’2022. The research is partially sponsored by the Japan Society of Civil
Engineers. The authors also acknowledge the participants of CRDDC - Madhavendra Sharma,
Dr. Muneer Al- Hammadi, Mamoona Birkhez Shami, Dr. Alex Klein-Paste, Dr. Helge Mork, and
Dr. Frank Lindseth, from Norwegian University of Science and Technology, Norway, Dr. Van
Vung Pham and Du Nguyen from Sam Houston State University, Texas, United States, and
16
Jingtao Zhong, Hanglin Cheng, Jing Zhang from Southeast University, China, for contributing
the respective datasets.
References
1. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., & Sekimoto, Y. RDD2020: An
annotated image dataset for automatic road damage detection using deep learning.
Data in brief, 36, 107133. (2021).
2. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Omata, H., Kashiyama, T., Seto, T.,
Mraz, A., & Sekimoto, Y. RDD2020: An Image Dataset for Smartphone-based Road
Damage Detection and Classification, Mendeley Data,
https://dx.doi.org/10.17632/5ty2wb6gvg.2 (2021).
3. Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., & Omata, H. Road damage
detection and classification using deep neural networks with smartphone images.
Computer‐Aided Civil and Infrastructure Engineering, 33(12), 1127-1141. (2018).
4. Maeda, H., Kashiyama, T., Sekimoto, Y., Seto, T., & Omata, H. Generative adversarial
network for road damage detection. Computer‐Aided Civil and Infrastructure
Engineering, 36(1), 47-60. (2021).
5. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Mraz, A., Kashiyama, T., & Sekimoto,
Y. Transfer learning-based road damage detection for multiple countries. Preprint at
https://arxiv.org/abs/2008.13101. (2020).
6. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Mraz, A., Kashiyama, T., & Sekimoto,
Y. Deep learning-based road damage detection and classification for multiple
countries. Automation in Construction, 132, 103935. 10.1016/j.autcon.2021.103935.
(2021).
7. Maintenance Guidebook for Road Pavements, 2013 edition, Technical Report, Japan
Road Association, Japan, (2013).
8. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Omata, H., Kashiyama, T., &
Sekimoto, Y. Global Road Damage Detection: State-of-the-art Solutions. IEEE
International Conference on Big Data (Big Data), Atlanta, GA, USA, 2020, pp. 5533-
5539, https://dx.doi.org/10.1109/BigData50022.2020.9377790. (2020).
9. Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A.
The pascal visual object classes challenge: A retrospective. International journal of
computer vision, 111(1), 98-136. (2015).
