Structure‐PoseNet for identification of dense dynamic displacement and three‐dimensional poses of structures using a monocular camera. (23rd August 2021) - Zhao, Jin; Hu, Fangqiao; Xu, Yang; Zuo, Wangmeng; Zhong, Jiwei; Li, Hui
Abstract: This study proposesStructure‐PoseNet to recognize three‐dimensional (3D) poses and dense dynamic displacement of a structure with known geometry using a monocular camera. The 3D poses are employed to calculate the structural displacement of both visible and invisible elements in videos. The proposed framework consists of two consecutive deep learning modules, CompNet and ParaNet, to provide semantic image segmentation and extract pose parameters, respectively. CompNet converts natural video images into semantic classification masks. ParaNet uses the recognized masks from CompNet as input and outputs the pose parameters. The dense dynamic displacement at measurement points with an intervaldistance of 2% of the size of the structure (along the direction with the maximum size) is then obtained using the coordinates of each 3D model vertex. The identification accuracy is discussed for different cases. Furthermore, the performance of the proposed method was validated through the shaking table test results of the two scale models. Finally, the proposed method was applied to a full‐scale suspension bridge and the Tacoma Narrows Bridge (1940) using Internet videos.