Research on mine vehicle tracking and detection technology based on YOLOv5

Abstract

Vehicle tracking detection, recognition and counting is an important part of vehicle analysis. Designing such a model with excellent performance is difficult. The traditional target detection algorithm based on artificial features has poor generalization ability and robustness. In order to take use the deep learning method for vehicle tracking detection, recognition and counting, this paper proposes a vehicle detection method based on yolov5. This method uses the deep learning technology, takes the running vehicles video as the research object, analysis the target detection algorithm, proposes a vehicle detection framework and platform. The relevant detection algorithm of the platform designed in this paper has great adaptability, when displayed under various conditions, such as heavy traffic, night environment, multiple vehicles overlap with each other, partial loss of vehicles, etc. it has good performance. The experimental results show that the algorithm can accurately segment and identify vehicles according to the edge contour of vehicles. It can take use the materials includes pictures, videos, and real-time monitoring, and has a high recognition rate in real-time performance.

Keywords

• Vehicle detection
• curve segmentation
• yolov5
• real-time processing