Structural displacement estimation by a hybrid computer vision approach

• Published in: Mechanical systems and signal processing Vol. 204; p. 110754
• Main Authors: Gao, Xiang, Ji, Xiaodong, Zhang, Yi, Zhuang, Yuncheng, Cai, Enjian
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023
• Subjects: Computer vision; Correlation template matching; Data fusion; Displacement estimation; Optical flow; Computer vision; Data fusion; Displacement estimation; Optical flow; Correlation template matching
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2023.110754

•A novel structural displacement estimation approach integrating dense optical flow and template matching was proposed.•The proposed approach was evaluated in two large-scale shaking table tests.•The hybrid approach eliminated displacement drift caused by cumulative errors.•Stable and reliable estimation of structural displacement across various environments was achieved. Structural displacement is an important indicator of structural safety in structural health monitoring (SHM). As one of the vision-based methods, optical flow can provide displacement measurement of pixels in images. However, estimating structural displacement by predicting the optical flow between the current frame and the initial frame may be subject to limited accuracy due to environmental variations. Alternatively, calculating structural velocity by predicting the optical flow of adjacent frames and integrating to obtain displacement may introduce displacement drift attributable to cumulative errors. This study proposes a hybrid structural displacement estimation method to eliminate the effects of environmental variations and cumulative errors. In comparison to the existing methods, the novelty of the proposed method is to effectively integrate deep learning-based dense optical flow and correlation template matching (CM), for achieving both high accuracy and improved robustness. Deep learning-based dense optical flow was used for optical flow prediction between adjacent frames through correlation calculations and iterative updating to obtain the structural velocity. Pyramid-accelerated CM was employed to locate the regions of interest (ROI) of the structure in each frame, and the structural displacement was then estimated by counting temporal changes in these locations. By fusing estimated structural velocity and displacement using the Kalman filter, optimized structural displacement results were obtained, and temporal cumulative errors using dense optical flow could be eliminated. The proposed method was validated in an indoor shaking table test of a three-story reinforced concrete structure, and an outdoor shaking table test of a cold-formed steel wall system. The results indicated that the proposed method reduced the root mean square error of estimated displacement by over 89% compared with dense optical flow and by over 36% compared with CM. Furthermore, the proposed method was able to process 1080p high-definition images at a rate of 5.43 frames per second, indicating its high efficiency for applications.