Computer Vision-based Algorithm for Deformation Monitoring in Bridge Construction Research
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Bibliographic Details
| Author(s): |
Weixin Yuan
(China Railway Major Bridge Engineering Group Co., Ltd., Tianjin, China)
Wei Gao (China Railway Major Bridge Engineering Group Co., Ltd., Tianjin, China) Wei Wang (China Railway Major Bridge Engineering Group Co., Ltd., Tianjin, China) Jishu Wu (Department of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai, China) Chao Liu (Department of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai, China) |
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| Medium: | conference paper | ||||
| Language(s): | English | ||||
| Conference: | IABSE Congress: Beyond Structural Engineering in a Changing World, San José, Cost Rica, 25-27 Seotember 2024 | ||||
| Published in: | IABSE Congress San José 2024 | ||||
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| Page(s): | 778-784 | ||||
| Total no. of pages: | 7 | ||||
| DOI: | 10.2749/sanjose.2024.0778 | ||||
| Abstract: |
The aim of this paper is to present a methodology for the detection of deformations in bridges during construction. Using the YOLOv5 model, a detection and localization model was trained to identify checkerboard targets. Relevant features were then extracted from the detected target patterns. To determine changes in target poses, the correspondence was established between 3D world coordinates and 2D pixel coordinates. Utilizing this correspondence, the real-time target poses were calculated, providing valuable insights into the magnitude and direction of deformations. The proposed visual deformation measurement method is dependent on the target and the camera, enabling real-time and cost-effective measurements of target displacement. The experimental results demonstrated the efficacy of our approach, which exhibited remarkable accuracy and robustness. |
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| Keywords: |
computer vision artificial intelligence convolutional neural networks bridge construction monitoring structural deformation monitoring
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