Research on High-Precision Detection of Road Vehicles in UAV Aerial Photography Based on YOLOv7

UAV aerial photography and imaging technology combines the aerial superiority of UAV and the intelligent analysis ability of computer vision, which can be widely used in geographic information, agriculture, environmental protection, infrastructure monitoring and other fields, providing efficient and accurate data support for all walks of life, which has important applications and significance. However, there are problems such as the size and accuracy of target recognition cannot be taken into account, and there are problems such as large difference between target detection object recognition and actual size, and there are problems such as occlusion. In order to improve the accuracy of UAV aerial photography in real-time vehicle monitoring, this paper inserts an efficient Vehicle Detection Aggregation Network (ELAN-VD) into its backbone layer network based on the YOLOv7-tiny model, so as to achieve real-time monitoring of vehicles on the road with low cost, high recognition speed and high recognition accuracy. Experiments show that the improved model mAP can be as high as 58%, which is much higher than that of the YOLOv7-tiny model, and the low cost of this model further illustrates its practical application value.