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Computer Vision-based Algorithm for Deformation Monitoring in Bridge Construction Research

 Computer Vision-based Algorithm for Deformation Monitoring in Bridge Construction Research
Auteur(s): , , , ,
Présenté pendant IABSE Congress: Beyond Structural Engineering in a Changing World, San José, Cost Rica, 25-27 Seotember 2024, publié dans , pp. 778-784
DOI: 10.2749/sanjose.2024.0778
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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 identif...
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Détails bibliographiques

Auteur(s): (China Railway Major Bridge Engineering Group Co., Ltd., Tianjin, China)
(China Railway Major Bridge Engineering Group Co., Ltd., Tianjin, China)
(China Railway Major Bridge Engineering Group Co., Ltd., Tianjin, China)
(Department of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai, China)
(Department of Bridge Engineering, College of Civil Engineering, Tongji University, Shanghai, China)
Médium: papier de conférence
Langue(s): anglais
Conférence: IABSE Congress: Beyond Structural Engineering in a Changing World, San José, Cost Rica, 25-27 Seotember 2024
Publié dans:
Page(s): 778-784 Nombre total de pages (du PDF): 7
Page(s): 778-784
Nombre total de pages (du PDF): 7
DOI: 10.2749/sanjose.2024.0778
Abstrait:

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.