Vision-based displacement measurement method of large-scale bridges using tilt shift camera and fast spatio-temporal context learning

Tracking vibration displacement across multiple points on large-scale bridges in the field poses significant challenges due to perspective distortion by camera tilting, interference from adverse environmental factors and low-resolution images by long distance measurement. To address these issues, this study proposed a novel vision-based displacement measurement framework that combined the Fast Spatio-Temporal Context Learning algorithm (FSTC) with tilt shift cameras to enhance visual tracking accuracy. A procedure was introduced to select suitable tilt shift cameras to eliminate the adverse effects of potential lens distortion. The FSTC algorithm, which integrated the Spatio-Temporal Context Learning algorithm (STC) and FastFlownet, was employed to improve the displacement tracking accuracy of the target object under fog occlusion and during long-distance measurements. Experimental results from slider and shaking table tests demonstrated that the FSTC algorithm outperformed the existing vision-based methods such as STC, Digital Image Correlation (DIC), and Kanade-Lucas-Tomasi (KLT) algorithms in tracking the displacement of the target object, effectively controlling the displacement error (e.g., RMSE and peak error) to below 3 mm. The FSTC algorithm was less sensitive to fog occlusion and exhibited enhanced efficiency in object tracking. Furthermore, the FSTC algorithm, in combination with image resolution reconstruction, enabled subpixel accuracy in tracking the displacement of the target object. A preliminary field application was conducted to track the vibration displacement of the Sanchaji Bridge under wind load, validating the feasibility and applicability of the proposed framework.