关键词: 目标检测, 深度学习, 目标数据集, 图像处理, 多标签

Abstract:

Aiming at the problem of vehicle object detection in highway scenes, an improved YOLOv4 network is proposed to detect vehicles in traffic scenes. Firstly, a multi-weather, multi-period, multi-scene vehicle dataset is proposed, and vehicle detection models are obtained based on the datasets. Secondly, a multi-label detection method is proposed, and a constraint relationship between multiple labels is established to obtain more complete vehicle information. Finally, an image stitching detection method is proposed, which connects multiple images through the stitching layer for vehicle detection, so as to improve the running speed of the network. Experimental results show that the diversified dataset improves the accuracy of vehicle detection, reduces the false detection and missed detection of vehicle objects, and the improved network structure greatly improves the detection speed. The above methods can provide references for vehicle detection and practical applications in highway scenes.

Key words: object detection, deep learning, object dataset, image process, multi-label