Embedded Vision‐Based Sensing System for Noncontact Cable Vibration Monitoring With IoT Technologies

Regular monitoring of cable forces is critical to ensuring the long‐term safety and performance of cable‐stayed bridges. While vision‐based methods offer noncontact, cost‐effective alternatives to traditional vibration‐based methods, most existing studies adopt an offline workflow in which videos are recorded and processed afterward. This study develops an embedded vision‐based sensing system for cable force monitoring. Unlike offline vision approaches, the system performs on‐site video acquisition, processing, and force estimation on‐site, enabling real‐time monitoring without external video transfer. First, an efficient and accurate visual object tracking (VOT) algorithm is proposed for real‐time displacement extraction from video sequences. We benchmark the algorithm’s accuracy and computational efficiency on a Jetson Orin Nano using a public shaking table test dataset. The results show that the algorithm achieves a good balance between accuracy and computational efficiency, making it suitable for deployment on edge computing devices. Subsequently, the cable vibration experiment indicates that the embedded vision‐based sensing system achieves maximum errors of 2.61% in cable frequency measurement and 5.68% in cable force estimation. In addition, the camera position did not materially affect system accuracy. Future work will enhance robustness under diverse field conditions and validate the system on full‐scale bridges.