Open lartpang opened 1 year ago
Reference: https://github.com/fyangneil/pavement-crack-detection
@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={IEEE Transactions on Intelligent Transportation Systems},
year={2019},
publisher={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={IEEE Transactions on Intelligent Transportation Systems},
year={2019},
publisher={IEEE}
}
@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}
}
@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}
}
@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}
}
@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}
}
DeepCrack: A Deep Hierarchical Feature Learning Architecture for Crack Segmentation
We established a public benchmark dataset with cracks in multiple scales and scenes to evaluate the crack detection systems. All of the crack images in our dataset are manually annotated.