A Novel Detection Method Using YOLOv5 for Vehicle Target under Complex Situation

Abstract

Aiming at the problem of low accuracy of vehicle image detection and recognition caused by low visibility in foggy weather, an improved YOLOv5 algorithm is proposed. This algorithm adjusts the brightness and contrast of the image by adding the improved adaptive histogram equalization method to the image preprocessing, highlights the detailed information of vehicle image signs, and changes the backbone network standard convolution mode to the depth separable convolution method for model lightweight processing. By constructing the corresponding vehicle target detection data set, this paper is superior to the object detection model commonly used on the public data set in terms of performance and effectiveness, and draws the following conclusion from the comparison results of ablation experiments, the improved algorithm improves the detection accuracy of a single image and reduces the processing time.