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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 ! -
The RDD2022 data released through CRDDC is now also available on FigShare Repository ! Kindly cite if you are using the data or the information. -
RDD2022.zip contains 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: -
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)
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@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} }
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train.tar.gz contains Japan/India/Czech images and annotations. The format of annotations is the same as pascalVOC.
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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.
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trainedModels
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trainedModels -
SSD Inception V2 -
SSD MobileNet
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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
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Chiba -
Muroran -
Ichihara -
Sumida -
Nagakute -
Numazu
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How to download Road Crack Dataset -
The structure of the Dataset -
The statistical information of the dataset -
How to use trained models.