Study of Vehicle Detection and Tracking on Bridge by Improved YOLOv5 and Deep SORT
It is of great significance to know the real vehicle load condition for bridge design and intelligent mainte-nance.Therefore,based on computer vision technology and deep learning,the multi-vehicle detection and tracking algorithm on the bridge is established in this paper.Firstly,a vehicle appearance dataset containing multiple types is established by collecting traffic surveillance videos.Secondly,the multi-vehicles detection algorithms is estab-lished and trained and tested on the dataset.Then,the algorithm with the best performance is combined with the best tracking algorithm to complete the multi-vehicle target tracking on the bridge.Finally,based on the traffic monitoring data of a long-span bridge,the improved effect of the algorithm is verified,and the reliability and accu-racy of the proposed algorithm are verified.The experimental results show that the proposed multi-vehicle detection and tracking algorithm has high detection accuracy,better tracking effect and stability in video sequences,which can successfully complete the continuous tracking of multi-vehicles on bridges.The research results can provide da-ta reference for the subsequent intelligent management and maintenance of bridges.
国家科技期刊平台
NSTL
万方数据
