Enhancing Cable Vibration Measurement at Long Distances Through Super-Resolution Reconstruction and Target Foreground Segmentation

This paper presents a comprehensive non-contact computer vision-based system for monitoring cable vibrations in cable-supported bridges, addressing challenges related to low image resolution and feature extraction difficulties at long distances. The proposed system utilizes deep learning techniques to enhance cable vibration recognition accuracy and offers a practical solution for cable monitoring without the need for target assistance. The core of the system is a novel two-stage model, which combines a super-resolution (SR) video reconstruction algorithm with state-of-the-art Resnet-34 and Swin-B models for precise target foreground segmentation. This approach significantly improves the recognition of target details and enhances the accuracy of cable vibration data in monitoring videos. Furthermore, a phase-based motion estimation (PME) algorithm is employed for precise cable vibration measurement. Field tests conducted on two cable-supported bridges validate the effectiveness of the system. The results demonstrate superior accuracy and noise immunity compared to traditional methods, achieving sub-pixel level precision with a maximum error rate below 2%. This system represents a significant advancement in non-contact structural health monitoring for long-distance cable vibration monitoring in cable-supported bridges.