Aircraft Object Detection Based on Improved YOLOv4 Algorithm for Remote Sensing Images
Aiming at the problem of low accuracy of aircraft target detection on remote sensing images,this paper deepens the PANet feature fusion network structure to make the YOLOv4 algorithm more sensitive to the detection of small objects,thereby im-proving the average detection precision of the algorithm.In addition,the K-means++ algorithm is used to generate adaptive data sets.In order to reduce the redundancy of the YOLOv4 detection algorithm in the calculation of the bounding box regression loss.Comparative experiments on the RSOD data set show that the AP value of the improved algorithm reaches 80.25%.In particular,the improved YOLOv4 algorithm has a higher confidence score for small object detection respectively.
国家科技期刊平台
NSTL
万方数据
