Speed Monitoring of Heavy Vehicles on Construction Plants by Fusing Camera Visual Image with UAV LiDAR Point Cloud
Zehua Wu, Ying Chen, Xiaoliang Meng, Jieyan Sun, Yuhan Zhu, Tinghao Li · 2023
Driving heavy vehicles at high speeds through construction sections can pose significant dangers. Implementing a ground speed radar system during construction progress is challenging due to the high costs associated with installation and maintenance. This paper proposes an innovative and cost-effective method for monitoring vehicle speed on construction sites by combining 2D/3D data. This method involves collecting 3D point cloud data using UAV LiDAR and capturing 2D visual image data using IoT cameras placed around construction sections. The EPnP algorithm is employed to calculate the matching results of pixel coordinates and world coordinates using the 2D and 3D data. Subsequently, the YOLOv5 object detection algorithm is combined with the DeepSORT tracking algorithm to enable real-time detection and tracking of heavy vehicles. Lastly, vehicle speed monitoring is conducted by analyzing the 2D/3D coordinate matching and the results of vehicle detection tracking. Pertinent experiments conducted in real scenarios validate that the method achieves a maximum error of 7.24% and an average relative error of 4.16%, satisfying the requirements for real-time monitoring of heavy vehicle speeds on construction sites.