Large field monitoring system of vehicle load on long-span bridge based on the fusion of multiple vision and WIM data

Vehicle Load Monitoring (VLM) on entire long-span bridge decks presents significant challenges due to the spatial and temporal randomness of vehicles. Existing VLM systems often suffer from limited viewing coverage and poor continuity of multi-vehicle tracking methods. This paper proposes a VLM system consisting of multivision image pre-processing, modified YOLO-v4 model, kinematics-enhanced vehicle tracking algorithm, and data fusion method between vision and Weigh-In-Motion sub-systems. The system was tested on a long-span bridge using six cameras, achieving vehicle monitoring of entire deck. The precision of multi-vehicle tracking achieved 99.28% built upon YOLO-v4 model with 96.2% mean Average Precision (mAP). Comparative results demonstrate that the modified YOLO-v4 model outperforms state-of-the-art approaches, and our proposed tracking method surpasses other methods. Our proposed system offers a comprehensive solution for VLM on entire bridge deck, overcoming the limitations of existing methods. Future work could extend the system's capability to include complex traffic patterns.

Automated summary

This study proposes a vehicle load monitoring system for entire long-span bridge decks, which uses multivision image pre-processing, a modified YOLO-v4 model, a kinematics-enhanced vehicle tracking algorithm, and a data fusion method between vision and Weigh-In-Motion subsystems. The system was tested on a long-span bridge using six cameras, achieving vehicle monitoring of the entire deck with a multi-vehicle tracking precision of 99.28% and a mean Average Precision (mAP) of 96.2% based on the YOLO-v4 model. Comparative results show that the modified YOLO-v4 model outperforms state-of-the-art approaches, and our proposed tracking method surpasses other methods. Our proposed system offers a comprehensive solution for vehicle load monitoring on entire bridge decks, overcoming the limitations of existing methods. Future work could extend the system's capability to include complex traffic patterns.