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Smart Structures and Systems Volume 33, Number 2, February 2024 , pages 105-118 DOI: https://doi.org/10.12989/sss.2024.33.2.105 |
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UAV-based bridge crack discovery via deep learning and tensor voting |
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Xiong Peng, Bingxu Duan, Kun Zhou, Xingu Zhong, Qianxi Li and Chao Zhao
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| Abstract | ||
| In order to realize tiny bridge crack discovery by UAV-based machine vision, a novel method combining deep learning and tensor voting is proposed. Firstly, the grid images of crack are detected and descripted based on SE-ResNet50 to generate feature points. Then, the probability significance map of crack image is calculated by tensor voting with feature points, which can define the direction and region of crack. Further, the crack detection anchor box is formed by non-maximum suppression from the probability significance map, which can improve the robustness of tiny crack detection. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method in the Xiangjiang-River bridge inspection. Compared with the original tensor voting algorithm, the proposed method has higher accuracy in the situation of only 1-2 pixels width crack and the existence of edge blur, crack discontinuity, which is suitable for UAV-based bridge crack discovery. | ||
| Key Words | ||
| bridge crack; feature points; deep learning; tensor voting; unmanned aerial vehicle | ||
| Address | ||
| (1) Xiong Peng, Bingxu Duan, Xingu Zhong: Hunan Provincial Key Laboratory of Structures for Wind Resistance and Vibration Control, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China; (2) School of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China. | ||