Crack Segmentation

For the details of the work, the readers are refer to the paper "Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection" (FPHB), T-ITS 2019. You can find the paper in https://www.researchgate.net/publication/330244656_Feature_Pyramid_and_Hierarchical_Boosting_Network_for_Pavement_Crack_Detection or https://arxiv.org/abs/1901.06340. The pavement crack datasets used in paper, crack detection results on each datasets, trained model, and crack annotation tool are stored in Google Drive, One Drive, and Daidu Yunpan extract code: jviq.

CrackForest Dataset is an annotated road crack image database which can reflect urban road surface condition in general. If you use this crack image dataset, we appreciate it if you cite an appropriate subset of the following papers: @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} } @inproceedings{cui2015pavement, title={Pavement Distress Detection Using Random Decision Forests}, author={Cui, Limeng and Qi, Zhiquan and Chen, Zhensong and Meng, Fan and Shi, Yong}, booktitle={International Conference on Data Science}, pages={95--102}, year={2015}, organization={Springer} } 2.License. The dataset is made available for non-commercial research purposes only. 3.History. Version 1.0 (2015/09/29) initial version

We present the CrackVision12k dataset, a collection of 12,000 crack images derived from 13 publicly available crack datasets. The individual datasets were too small to effectively train a deep learning model. Moreover, the masks in each dataset were annotated using different standards, so unifying the annotations was necessary. To achieve this, we applied various image processing techniques to each dataset to create masks that follow a consistent standard. Crack datasets inherently suffer from class imbalance. To mitigate this issue, we selected images containing crack pixels of more than 5000 pixels and applied data augmentation techniques such as Gaussian noise and rotation. Finally, there is a corresponding refined ground truth for each crack image across the dataset to ensure uniformity and reliability. The 13 datasets we combined are as follows: Aigle-RN, ESAR, LCMS, CRACK500, CrackLS315, CRKWH100, CrackTree260, DeepCrack, GAPS384, Masonry, Stone331, CFD, and SDNet2018.

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.)