lartpang / awesome-segmentation-saliency-dataset

A collection of some datasets for segmentation / saliency detection. Welcome to PR...:smile:
https://lartpang.github.io/awesome-segmentation-saliency-dataset/README.html
MIT License
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Crack Segmentation Datasets #20

Open lartpang opened 1 year ago

lartpang commented 1 year ago

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.

@article{liu2019deepcrack,
  title={DeepCrack: A Deep Hierarchical Feature Learning Architecture for Crack Segmentation},
  author={Liu, Yahui and Yao, Jian and Lu, Xiaohu and Xie, Renping and Li, Li},
  journal={Neurocomputing},
  volume={338},
  pages={139--153},
  year={2019},
  doi={10.1016/j.neucom.2019.01.036}
}
lartpang commented 1 year ago

Reference: https://github.com/fyangneil/pavement-crack-detection

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={IEEE Transactions on Intelligent Transportation Systems},
    year={2019},
    publisher={IEEE}
}

GAPs384

@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}
}

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}
}