Intelligent Identification of Vehicle Tracking and Load Spatio-temporal Distribution in Long-span Bridge

Vehicle load is one of the most important loads of long-span bridges, and it is also the main cause of fatigue deterioration of most bridges. However, the bridge weigh-in-motion system is expensive and cannot be distributed across the bridge, which means the dynamic identification of bridge vehicle load distribution information is still a challenging problem. This paper introduced computer vision and deep learning technologies to meet the needs of long-span bridge structural health monitoring, and established an integrated intelligent identification system for bridge vehicle load spatio-temporal distribution. Firstly, we studied the vehicle identification method based on traffic monitoring data and deep target detection network, trained the YOLOv7 deep network for vehicle target detection tasks, and obtained vehicle images containing information such as vehicle type and time in single camera through the trained model. Then, we introduced the HardNet depth feature descriptor to establish an image point feature matching method, designed a searching and matching strategy through distributed surveillance video data to achieve the matches of vehicle image data corresponding to multiple monitors in the traffic flow direction, and the vehicle position was estimated by linear interpolation of the monitoring blind area to obtain the spatio-temporal distribution of vehicles on the bridge. Finally, the methods were integrated to establish the vehicle load spatio-temporal distribution identification system. This system can automatically output the spatio-temporal distribution of vehicle load and visualization results combining with dynamic weighing data, realizing an integrated process from monitoring data to vehicle load spatio-temporal distribution. In this paper, the monitoring data of Jiujiang Yangtze River Bridge was used for verification. The results show that the system can achieve vehicle identification and tracking based on video data, with computational time less than the duration of the input video and an accuracy rate of 97. 62% for large vehicle matching, allowing for rapid and accurate identification of vehicle load distribution. The system is of great significance to ensure the safety of bridge service and has broad application prospect. © 2024 Chang'an University. All rights reserved.