Abstract
The road crack detection remains a crucial task in the road maintenance and safety management. However, due to the diversity and complexity of cracks, achieving the fine-grained and accurate segmentation is still challenging. To this end, this paper proposes a novel physically informed prior-guided crack segmentation method. Specifically, we employ the dynamic snake convolution to enhance the segmentation continuity and consistency. Moreover, a prior information is injected to supplement the morphology and structural features of road cracks, aiming to mitigate the miss detection of the binary-branched and webbed cracks. To ensure the continuity and completeness of cracks, a cross-correlation constraint is further designed. The constraint leverages the semantic consistence of the crack regions to promote the network to capture and segment small and complex cracks. Experimental validations on two datasets demonstrate that the proposed approach significantly outperforms state-of-the-art methods, achieving substantial improvements in the fine-grained detail and the continuity of the road crack segmentation.
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References
Kulkarni, S., Singh, S., Balakrishnan, D., Sharma, S., Devunuri, S., Korlapati, S.C.R.: Crackseg9k: a collection and benchmark for crack segmentation datasets and frameworks. In: European Conference on Computer Vision, pp. 179–195. Springer (2022)
Fan, R., Bocus, M.J., Zhu, Y., Jiao, J., Wang, L., Ma, F., Cheng, S., Liu, M.: Road crack detection using deep convolutional neural network and adaptive thresholding. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 474–479. IEEE (2019)
Hoang, N.D., et al.: Detection of surface crack in building structures using image processing technique with an improved Otsu method for image thresholding. Adv. Civil Eng. (2018)
Talab, A.M.A., Huang, Z., Xi, F., HaiMing, L.: Detection crack in image using Otsu method and multiple filtering in image processing techniques. Optik 127(3), 1030–1033 (2016)
Akagic, A., Buza, E., Omanovic, S., Karabegovic, A.: Pavement crack detection using Otsu thresholding for image segmentation. In: 2018 41st international convention on information and communication technology, electronics and microelectronics (MIPRO), pp. 1092–1097. IEEE (2018)
Hu, Y., Zhao, C.x., Wang, H.n.: Automatic pavement crack detection using texture and shape descriptors. IETE Tech. Rev. 27(5), 398–405 (2010)
Shi, Y., Cui, L., Qi, Z., Meng, F., Chen, Z.: Automatic road crack detection using random structured forests. IEEE Trans. Intell. Transp. Syst. 17(12), 3434–3445 (2016)
Hong, Z., Yang, F., Pan, H., Zhou, R., Zhang, Y., Han, Y., Wang, J., Yang, S., Chen, P., Tong, X., et al.: Highway crack segmentation from unmanned aerial vehicle images using deep learning. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2021)
Özgenel, Ç.F., Sorguç, A.G.: Performance comparison of pretrained convolutional neural networks on crack detection in buildings. In: ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, vol. 35, pp. 1–8. IAARC Publications (2018)
Oliveira, H., Correia, P.L.: Automatic road crack segmentation using entropy and image dynamic thresholding. In: 2009 17th European Signal Processing Conference, pp. 622–626. IEEE (2009)
Goh, T.Y., Basah, S.N., Yazid, H., Safar, M.J.A., Saad, F.S.A.: Performance analysis of image thresholding: Otsu technique. Measurement 114, 298–307 (2018)
Hsieh, Y.A., Tsai, Y.J.: Machine learning for crack detection: review and model performance comparison. J. Comput. Civ. Eng. 34(5), 04020038 (2020)
Alfarrarjeh, A., Trivedi, D., Kim, S.H., Shahabi, C.: A deep learning approach for road damage detection from smartphone images. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5201–5204. IEEE (2018)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 580–587 (2014)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Advances In Neural Inf. Process. Syst. 28 (2015)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, part III 18, pp. 234–241. Springer (2015)
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022 (2021)
Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: transformers make strong encoders for medical image segmentation (2021). arXiv:2102.04306
Liu, H., Miao, X., Mertz, C., Xu, C., Kong, H.: Crackformer: transformer network for fine-grained crack detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3783–3792 (2021)
Qi, Y., He, Y., Qi, X., Zhang, Y., Yang, G.: Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6070–6079 (2023)
Zhang, R., Feng, X., Yang, L., Chang, L., Xu, C.: Global sparse gradient guided variational retinex model for image enhancement. Signal Process.: Image Commun. 58, 270–281 (2017)
Acknowledgement
This work is supported by the project of Science and Technology Development Plan in Hangzhou under Grant No. 202202B38, and supported by the Fundamental Research Funds for the Central Universities under Grant No.20103248078, and supported by the Key Laboratory of Cognitive Radio and Information Processing, Ministry of Education under Grant No.CRKL230204.
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Li, S., Gou, S., Yao, Y., Chen, Y., Wang, X. (2025). Physically Informed Prior and Cross-Correlation Constraint for Fine-Grained Road Crack Segmentation. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15033. Springer, Singapore. https://doi.org/10.1007/978-981-97-8502-5_32
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DOI: https://doi.org/10.1007/978-981-97-8502-5_32
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