Advanced Computer Vision Technology for Real-Time Rotation Angle Monitoring of Bridge Bearings in Structural Health Assessment

Real-time perception of bearing rotation angles is essential for structural health assessment of bridges. However, existing vision-based rotation angle measurement methods exhibit limited robustness to time-varying operational conditions and tracking errors, particularly in practical applications of bridge monitoring. To address this limitation, this study presents an advanced computer vision-based monitoring technology for bridge bearing rotation angles by incorporating specifically configured retroreflective targets, an efficient target tracking approach, and a rotation angle calculation algorithm. Firstly, under LED illumination, retroreflective targets appear as bright, high-contrast features in the images, facilitating precise detection and tracking. Secondly, target centroids are tracked with sub-pixel accuracy through thresholding, edge extraction, and ellipse fitting. Lastly, the bearing rotation angle is calculated by analyzing the angle between the two characteristic lines formed by the target centroids. To validate the effectiveness of the proposed method, comprehensive numerical investigations were conducted, and the results showed that the proposed method maintained high accuracy across various imaging conditions. Additionally, comparative analysis with an existing advanced method also revealed that the proposed method exhibits superior measurement performance even under target tracking uncertainties. To investigate its feasibility and validate its practical effectiveness, a field application on an 80 m + 80 m continuous beam was conducted, and minute rotation angle measurements during 23 railway train drive-by events were obtained using the proposed method, yielding a root mean square error of 0.0008° and mean absolute error of 0.0007°. The successful development and field deployment demonstrate significant potential for advancing structural health monitoring technologies, contributing to intelligent infrastructure management through automated monitoring and early warning capabilities.