From my knowledge, the dataset used in the project is the largest crack segmentation dataset so far. It contains around 11.200 images that are merged from 12 available crack segmentation datasets.
The name prefix of each image is assigned to the corresponding dataset name that the image belong to. There're also images with no crack pixel, which could be filtered out by the file name pattern "noncrack*"
All the images are resized to the size of (448, 448).
The two folders images and masks contain all the images. The two folders train and test contain training and testing images splitted from the two above folder. The splitting is stratified so that the proportion of each dataset in the train and test folder are similar.
Citation Note: please cite the corresponding papers when using these datasets.
CRACK500:
@inproceedings{zhang2016road, title={Road crack detection using deep convolutional neural network}, author={Zhang, Lei and Yang, Fan and Zhang, Yimin Daniel and Zhu, Ying Julie}, booktitle={Image Processing (ICIP), 2016 IEEE International Conference on}, pages={3708--3712}, year={2016}, organization={IEEE} }' .
@article{yang2019feature, title={Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection}, author={Yang, Fan and Zhang, Lei and Yu, Sijia and Prokhorov, Danil and Mei, Xue and Ling, Haibin}, journal={arXiv preprint arXiv:1901.06340}, year={2019} }
GAPs384:
@inproceedings{eisenbach2017how, title={How to Get Pavement Distress Detection Ready for Deep Learning? A Systematic Approach.}, author={Eisenbach, Markus and Stricker, Ronny and Seichter, Daniel and Amende, Karl and Debes, Klaus and Sesselmann, Maximilian and Ebersbach, Dirk and Stoeckert, Ulrike and Gross, Horst-Michael}, booktitle={International Joint Conference on Neural Networks (IJCNN)}, pages={2039--2047}, year={2017} }
CFD:
@article{shi2016automatic, title={Automatic road crack detection using random structured forests}, author={Shi, Yong and Cui, Limeng and Qi, Zhiquan and Meng, Fan and Chen, Zhensong}, journal={IEEE Transactions on Intelligent Transportation Systems}, volume={17}, number={12}, pages={3434--3445}, year={2016}, publisher={IEEE} }
AEL:
@article{amhaz2016automatic, title={Automatic Crack Detection on Two-Dimensional Pavement Images: An Algorithm Based on Minimal Path Selection.}, author={Amhaz, Rabih and Chambon, Sylvie and Idier, J{'e}r{^o}me and Baltazart, Vincent} }
cracktree200:
@article{zou2012cracktree, title={CrackTree: Automatic crack detection from pavement images}, author={Zou, Qin and Cao, Yu and Li, Qingquan and Mao, Qingzhou and Wang, Song}, journal={Pattern Recognition Letters}, volume={33}, number={3}, pages={227--238}, year={2012}, publisher={Elsevier} }
https://github.com/alexdonchuk/cracks_segmentation_dataset
https://github.com/yhlleo/DeepCrack
https://github.com/ccny-ros-pkg/concreteIn_inpection_VGGF
(Citing from https://github.com/khanhha/crack_segmentation.)