In Synthetic Aperture Radar(SAR)image aircraft target detection and recognition,the discrete characteristics of aircraft target images and the similarity between structures can reduce the accuracy of aircraft detection and recognition.A SAR image aircraft target detection and recognition network with enhanced target area features is proposed in this paper.The network consists of three parts:Feature Protecting Cross Stage Partial Darknet(FP-CSPDarnet)for protecting aircraft features,Feature Pyramid Net with Adaptive fusion(FPN-A)for adaptive feature fusion,and Detection Head for target area scattering feature extraction and enhancement(D-Head).FP-CSPDarnet can effectively protect the aircraft features in SAR images while extracting features;FPN-A adopts multi-level feature adaptive fusion and refinement to enhance aircraft features;D-Head effectively enhances the identifiable features of the aircraft before detection,improving the accuracy of aircraft detection and recognition.The experimental results using the SAR-ADRD dataset have demonstrated the effectiveness of the proposed method,with an average accuracy improvement of 2.0％compared to the baseline network YOLOv5s.
关键词
合成孔径雷达/飞机目标检测与识别/YOLOv5s/飞机特征保护/特征增强
Key words
Synthetic Aperture Radar(SAR)/Aircraft target detection and recognition/YOLOv5s/Aircraft feature protection/Target area feature enhancement
维普
万方数据