Research on vehicle target detection method based on YOLOv5

Vehicle target detection is a key research hotspot in the field of computer vision. At present, with the continuous development of deep learning and artificial intelligence, some excellent vehicle target detection algorithms such as YOLOv5, YOLOv4 and YOLOv3 have emerged. Therefore, in order to solve the problem of low accuracy of vehicle target detection, ensure the safety of vehicles on the road and achieve target detection more accurately. This paper provides a YoloV5-based method for detecting car objects and an improved algorithm that uses large-scale internal fusion techniques. Finally, the vehicle target detection accuracy of the improved YOLOv5 algorithm is effectively improved through experimental comparison and analysis. This is of great practical significance for promoting the development of target detection algorithms.