Aerial Target Detection Based on the Improved YOLOv3 Algorithm

The YOLOv3 algorithm is directly used to detect aerial targets with complex backgrounds, which leads to low detection accuracy and high missed detection rate. This paper proposes an improved YOLOv3 algorithm to detect aerial targets. Because the aerial target is small and its pixel is low, and its features are not obvious, this paper first uses the K-means clustering algorithm to analyze the dataset, selecting the appropriate number and size of the anchor boxes, and then improving the network detection scale to establish the feature fusion target detection layer. In the dataset of PETS200S-Tracking and DOTA, the improved YOLOv3 algorithm and the YOLOv3 algorithm are compared. The results show that the improved network can effectively detect the aerial target, and its precision and recall rate are significantly improved.