A computer vision and dynamic strain fusion approach for urban bridge weigh-in-motion

Dynamic weighing systems are crucial for bridge safety, yet current weighing systems are either too costly for widespread urban use or unable to track vehicles reliably in dense traffic. Here we present a video-strain inverse estimation system that combines computer vision with structural strain sensing to measure dynamic vehicle loads on bridges. The method employs a lightweight deep learning detector to recognize vehicle features, a multi-object tracking model to capture trajectories and lane positions, and a developed analytical algorithm to estimate vehicle weight from measured strains. We validated the system with field data from a heavily trafficked urban bridge. The system achieves vehicle recognition with near-baseline accuracy while using only 17% of the original model parameters and running 1.72 times faster, identifies lanes with complete accuracy with a missed detection rate of just 0.56%, and estimates total vehicle weights within 2% error. This low-cost and reliable approach advances intelligent bridge monitoring and supports digital twins of critical urban infrastructure.