Abstract
The detection of road cracks is the main basis of highway maintenance, and the noise, shadows, and irregularities of road images will bring great challenges to traditional detection. Therefore, we propose a multi-source attention U-net network, which can effectively avoid these interferences and get satisfactory results. In this method, we use transfer learning to make up for the lack of data, then use the U-net add attention mechanism to increase the weights of the cracks, and finally get more accurate results through model fusion. To prove the effectiveness of the method, we verify it by comparative experiments, and the experimental results show that the proposed approach is superior to the state of the art method in crack detection task.
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Acknowledgement
This work was partially funded by Natural Science Foundation of Jiangsu Province under grant No. BK20191298, National Key R & D Program of China under grant no. 2018YFC0407106, Key Laboratory of Coastal Disaster and Protection of Ministry of Euducation, Hohai University under grant no. 201905 and Fundamental Research Funds for the Central Universities under Gran No. B200202175.
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Wang, J., Liu, F., Yang, W., Xu, G., Tao, Z. (2020). Pavement Crack Detection Using Attention U-Net with Multiple Sources. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_55
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DOI: https://doi.org/10.1007/978-3-030-60639-8_55
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