[2024-05-15]: ORDDC'2024 - Announcement: Following the success of GRDDC'2020 and CRDDC'2022, another BigData Cup in the form of road damage detection challenge, ORDDC'2024, is open now! Associated conference: IEEE BigData'2024. Venue: Washington, DC, USA!
[2024-03-11]: CRDDC'2022 Detailed Review: Curious about -- What can we learn from Cross-country collaborations and Winning Strategies? Check out our latest article From global challenges to local solutions: A review of cross-country collaborations and winning strategies in road damage detection providing complete details. Use this link for free access till April 26, 2024!
[2023-09-29]: CRDDC'2022 Winners and Proposed Solutions: Check out the CRDDC article summarizing details of winners and proposed solutions here!
[2022-12-18]: CRDDC'2022 culminated successfully! New leaderboards available on the website can still be utilized to perform more experiments.
[2022-09-29]: Data Article for RDD2022: The article for data released through CRDDC'2022 can be accessed here!
[2022-09-29]: CRDDC'2022: Deadline for Phase 3 and 4 has been extended! Submissions will be accepted till Oct 5, 2022.
[2022-09-29]: CRDDC'2022: The submission links for phase 4 (Report and Source Code) have been enabled!
[2022-08-30]: CRDDC'2022: The submission link for phase 3 has been enabled! Users need to LogIn to access!
[2022-08-11]: The data for CRDDC'2022 has been released!
[2022-08-04]: The winners for CRDDC - Data Contribution phase have been announced!
[2022-07-04]: The deadline for CRDDC Phase 1 submissions has been extended to July 20, 2022!..............Register here!
[2022-06-07]: The IEEE Big Data Cup CRDDC'2022(https://crddc2022.sekilab.global/) is now open! More details are available here.
[2022-4-25]: Astonished with the Global Road Damage Detection Challenge (GRDDC'2020)?......................... Stay tuned!...................The GRDDC team is coming up with another challenge (CRDDC'2022) with exciting prizes and opportunities!
[2021-09-27]: Check out our latest article entitled Deep learning-based road damage detection and classification for multiple countries published in the journal Automation in Construction!
The article addresses automatic monitoring of road conditions for multiple countries and provides recommendations for reusing the Road Damage Detection data and models released by any country.
[2021-05-23]: Data Article for RDD2020: The article providing the details of Road Damage Dataset 2020(RDD2020) published in Data-in-Brief journal, can be accessed here!
[2021-03-23]: IEEE Big Data Cup - GRDDC 2020: The proceedings for 2020 IEEE International Conference on Big Data, Atlanta, GA, USA are available now! The published version of the paper summarizing GRDDC'2020 can be accessed here!
[2021-03-19]: RDD2020 dataset is now available at Mendeley in a citable and easy to share form!
[2020-12-14]: IEEE Big Data Cup - GRDDC 2020 culminated successfully! The paper Global Road Damage Detection: State-of-the-art Solutions provides the details of the challenge. Follow the project for further updates on the publications!
[2020-12-10]: IEEE Big Data Cup - GRDDC 2020: The workshop is being conducted in association with the IEEE International Conference on Big Data 2020! Check out the recordings at underline.io and the pictures here!
[2020-10-18]: IEEE Big Data Cup - Global Road Damage Detection Challenge 2020 - Submissions for two new leader-boards have been enabled to support experiments involving the India-Japan-Czech Road Damage data.
[2020-10-6]: IEEE Big Data Cup - Global Road Damage Detection Challenge 2020 - The names of winners have been anounced!
[2020-09-23]: Global Road Damage Detection Challenge 2020 - The link for submitting the source code has been enabled!
[2020-09-02]: The citation information and the article explaining the latest India-Japan-Czech (InJaCz) Road Damage Dataset, being used for IEEE BigData Cup Challenge 2020, is now available.
[2020-4-25]: Global Road Damage Detection Challenge 2020 will be held as one of the IEEE Bigdata Cup. How about joining the data cup now? Exciting prizes await you!
[2019-10-16]: Road Damage Dataset was awarded by the GIS Association of Japan. For more information, please check here.
[2018-12-10]: Road damage detection and classification challenge (one of the IEEE Bigdata Cup Challenge) was held in Seattle. 59 teams participated from 14 countries. For more information, please check here!
- Detailed Review of winning solutions (2024) - From global challenges to local solutions: A review of cross-country collaborations and winning strategies in road damage detection
- Summary Paper (Winners, tasks, Procedures) - Crowdsensing-based Road Damage Detection Challenge (CRDDC’2022)
- Data Article RDD2022: A multi-national image dataset for automatic Road Damage Detection
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The article providing detailed statistics and other information for data released through CRDDC'2022 can be accessed here!
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The RDD2022 data released through CRDDC is now also available on FigShare Repository! Kindly cite if you are using the data or the information.
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RDD2022.zipcontains train and test data from six countries: Japan, India, Czech Republic, Norway, United States, and China.- Images (.jpg) and annotations (.xml) are provided for the train set. The format of annotations is the same as pascalVOC.
- Only images are provided for test data.
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Supplementary files related to the RDD2020 data and CRDDC submissions:
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Links to download Country-specific data:
- RDD2022_Japan.zip (1022.9 MB - train and test)
- RDD2022_India.zip (502.3 MB - train and test)
- RDD2022_Czech.zip (245.2 MB - train and test)
- RDD2022_Norway.zip (9.9 GB - train and test)
- RDD2022_United_States.zip (423.8 MB - train and test)
- RDD2022_China_MotorBike.zip (183.1 MB - train and test)
- RDD2022_China_Drone.zip (152.8 MB - only train)
{D00: Longitudinal Crack, D10: Transverse Crack, D20: Aligator Crack, D40: Pothole}
@article{2024_ARYA_CRDDC_review,
title = {From global challenges to local solutions: A review of cross-country collaborations and winning strategies in road damage detection},
author = {Deeksha Arya and Hiroya Maeda and Yoshihide Sekimoto},
journal = {Advanced Engineering Informatics},
volume = {60},
pages = {102388},
year = {2024},
doi = {https://doi.org/10.1016/j.aei.2024.102388},
}
@inproceedings{arya2022crowdsensing,
title={Crowdsensing-based Road Damage Detection Challenge (CRDDC’2022)},
author={Arya, Deeksha and Maeda, Hiroya and Ghosh, Sanjay Kumar and Toshniwal, Durga and Omata, Hiroshi and Kashiyama, Takehiro and Sekimoto, Yoshihide},
booktitle={2022 IEEE International Conference on Big Data (Big Data)},
pages={6378--6386},
year={2022},
organization={IEEE}
}
@article{arya2022rdd2022,
title={RDD2022: A multi-national image dataset for automatic Road Damage Detection},
author={Arya, Deeksha and Maeda, Hiroya and Ghosh, Sanjay Kumar and Toshniwal, Durga and Sekimoto, Yoshihide},
journal={arXiv preprint arXiv:2209.08538},
year={2022}
}
@article{arya2021deep,
title={Deep learning-based road damage detection and classification for multiple countries},
author={Arya, Deeksha and Maeda, Hiroya and Ghosh, Sanjay Kumar and Toshniwal, Durga and Mraz, Alexander and Kashiyama, Takehiro and Sekimoto, Yoshihide},
journal={Automation in Construction},
volume={132},
pages={103935},
year={2021},
publisher={Elsevier}
}
@article{arya2021rdd2020,
title={RDD2020: An annotated image dataset for automatic road damage detection using deep learning},
author={Arya, Deeksha and Maeda, Hiroya and Ghosh, Sanjay Kumar and Toshniwal, Durga and Sekimoto, Yoshihide},
journal={Data in brief},
volume={36},
pages={107133},
year={2021},
publisher={Elsevier}
@inproceedings{arya2020global,
title={Global road damage detection: State-of-the-art solutions},
author={Arya, Deeksha and Maeda, Hiroya and Ghosh, Sanjay Kumar and Toshniwal, Durga and Omata, Hiroshi and Kashiyama, Takehiro and Sekimoto, Yoshihide},
booktitle={2020 IEEE International Conference on Big Data (Big Data)},
pages={5533--5539},
year={2020},
organization={IEEE}
}
Check out this video for details of GRDDC'2020 (Atlanta, GA, USA)!
The details of the Global Road Damage Detection Challenge (GRDDC) 2020, held as an IEEE Big Data Cup with a worldwide participation of 121 teams, are encapsulated in the paper Global Road Damage Detection: State-of-the-art Solutions.
Citation: Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Omata, H., Kashiyama, T., & Sekimoto, Y. (2020). Global Road Damage Detection: State-of-the-art Solutions. IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 2020, pp. 5533-5539, doi: 10.1109/BigData50022.2020.9377790.
Follow the project for further updates on the publications!
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train.tar.gzcontains Japan/India/Czech images and annotations. The format of annotations is the same as pascalVOC.
The data collection methodology, study area and other information for the India-Japan-Czech dataset are provided in our research papers entitled Deep learning-based road damage detection and classification for multiple countries, and RDD2020: An annotated image dataset for Automatic Road Damage Detection using Deep Learning!
The dataset utilizes the RDD-2019 data introduced in Generative adversarial network for road damage detection.
If you use or find our dataset and/or article useful, please cite the following:
- Latest Research Article: Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Mraz, A., Kashiyama, T., & Sekimoto, Y. (2021). Deep learning-based road damage detection and classification for multiple countries. Automation in Construction, 132, 103935. 10.1016/j.autcon.2021.103935.
- RDD-2020 Data Article: Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., & Sekimoto, Y. (2021). RDD2020: An annotated image dataset for automatic road damage detection using deep learning. Data in brief, 36, 107133. 10.1016/j.dib.2021.107133.
- RDD-2019 Article: Maeda, H., Kashiyama, T., Sekimoto, Y., Seto, T. and Omata, H. (2020). Generative adversarial network for road damage detection. Computer‐Aided Civil and Infrastructure Engineering, 36(1), pp.47-60.
- GRDDC Summary Paper: Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Omata, H., Kashiyama, T., & Sekimoto, Y. (2020). Global Road Damage Detection: State-of-the-art Solutions. IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 2020, pp. 5533-5539, doi: 10.1109/BigData50022.2020.9377790.
[dataset] Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Omata, H., Kashiyama, T., Seto, T., Mraz, A., & Sekimoto, Y. (2021), “RDD2020: An Image Dataset for Smartphone-based Road Damage Detection and Classification”, Mendeley Data, V1, doi: 10.17632/5ty2wb6gvg.1
arXiv Pre-print: Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Mraz, A., Kashiyama, T., & Sekimoto, Y. (2020). Transfer Learning-based Road Damage Detection for Multiple Countries. arXiv preprint arXiv:2008.13101.
{D00: Longitudinal Crack, D10: Transverse Crack, D20: Aligator Crack, D40: Pothole}
If you use or find out our dataset useful, please cite our paper in the journal of Computer-Aided Civil and Infrastructure Engineering:
Maeda, H., Kashiyama, T., Sekimoto, Y., Seto, T. and Omata, H. (2020). Generative adversarial network for road damage detection. Computer‐Aided Civil and Infrastructure Engineering, 36(1), pp.47-60.
Machine learning can produce promising results when sufficient training data are available; however, infrastructure inspections typically do not provide sufficient training data for road damage. Given the differences in the environment, the type of road damage and the degree of its progress can vary from structure to structure. The use of generative models, such as a generative adversarial network (GAN) or a variational autoencoder, makes it possible to generate a pseudoimage that cannot be distinguished from a real one. Combining a progressive growing GAN along with Poisson blending artificially generates road damage images that can be used as new training data to improve the accuracy of road damage detection. The addition of a synthesized road damage image to the training data improves the F‐measure by 5% and 2% when the number of original images is small and relatively large, respectively. All of the results and the new Road Damage Dataset 2019 are publicly available.
The structure of the Road Damage Dataset 2019 is the same as the previous one: Pascal VOC.
Please pay attention to the disk capacity when downloading.
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trainedModels
If you use or find out our dataset useful, please cite our paper in the journal of Computer-Aided Civil and Infrastructure Engineering:
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.
@article{maedaroad, title={Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images}, author={Maeda, Hiroya and Sekimoto, Yoshihide and Seto, Toshikazu and Kashiyama, Takehiro and Omata, Hiroshi}, journal={Computer-Aided Civil and Infrastructure Engineering}, publisher={Wiley Online Library} }
arXiv version is here.
Research on damage detection of road surfaces using image processing techniques has been actively conducted achieving considerably high detection accuracies. However, many studies only focus on the detection of the presence or absence of damage. However, in a real-world scenario, when the road managers from a governing body needs to repair such damage, they need to know the type of damage clearly to take effective action. In addition, in many of these previous studies, the researchers acquire their own data using different methods. Hence, there is no uniform road damage dataset available openly, leading to the absence of a benchmark for road damage detection. This study makes three contributions to address these issues. First, to the best of our knowledge, for the first time, a large-scale road damage dataset is prepared. This dataset is composed of 9,053 road damage images captured with a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. These images are captured in a wide variety of weather and illuminance conditions. In each image, the bounding box representing the location of the damage and the type of damage are annotated. Next, we use the state-of-the-art object detection method using convolutional neural networks to train the damage detection model with our dataset, and compare the accuracy and runtime speed on both, a GPU server and a smartphone. Finally, we show that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage dataset, our experimental results, and the developed smartphone application used in this study are made publicly available. This page introduces the road damage dataset we created.
Road Damage Dataset contains trained models and Annotated images. Annotated images are presented as the same format to PASCAL VOC.
- trainedModels
- SSD Inception V2
- SSD MobileNet
- RoadDamageDataset (dataset structure is the same format as PASCAL VOC)
- Adachi
- JPEGImages : contains images
- Annotations : contains xml files of annotation
- ImageSets : contains text files that show training or evaluation image list
- Chiba
- Muroran
- Ichihara
- Sumida
- Nagakute
- Numazu
- Adachi
Please pay attention to the disk capacity when downloading.
We also created the tutorial of Road Damage Dataset. In this tutorial, we will show you:
- How to download Road Crack Dataset
- The structure of the Dataset
- The statistical information of the dataset
- How to use trained models.
Please check RoadDamageDatasetTutorial.ipynb.
Our dataset is openly accessible by the public. Therefore, considering issues with privacy, based on visual inspection, when a person's face or a car license plate are clearly reflected in the image, they are blurred out.
Images on this dataset are available under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). The license and link to the legal document can be found next to every image on the service in the image information panel and contains the CC BY-SA 4.0 mark:
