- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | GAO Tian, JI Bohai, YUAN Zhouzhiyuan, LIU Yuhang, SUN Hongbin. Visual Localization Method Research of Fatigue Crack in Steel Box Girder Based on Registration Assisted by Weld[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20240432
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To address the localization problem of fatigue cracks in steel box girders with complex structures, a registration algorithm for crack and structure was proposed by leveraging the linear geometric features of welds. Consequently, the whole perception process for identifying, quantifying, and localizing the cracks was integrated and automated. Firstly, a dual-task deep neural network, CrackFormer-Hough, was established to identify the cracks and welds in two-dimensional (2D) images simultaneously. Then, the identified targets were reconstructed by stereo vision techniques to generate target point clouds and quantify their geometric features. Finally, by taking welds as the reference, the coordinate transformation relationship between the camera and structure space was obtained based on the coarse/fine point cloud registration algorithms. Based on structural coordinates, the crack segments from each scope were spliced and fused, and the complete crack morphology was plotted in the structural map. Field tests show that the identification network can overcome background interference and accurately distinguish the weld structure of specific regions by introducing the deep Hough transform to learn the scene semantics of steel box girders. By means of the obvious linear features of welds, the registration errors are controlled to about 1 mm with sparse point clouds. The fatigue crack perception performance is not sensitive to the camera poses. Under different lenses, the average localization error is within 5 mm, and the length measurement error is only about 1%.
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