摘要
Abstract
The precision and recall rate of object detection in synthetical aperture radar(SAR)image with complex background are both low and hard to raise.For above problems,an improved YOLOv8n algorithm is proposed which has better performance by using mobile inverted bottleneck convolution(MBConv),deformable attention(DAttention)and parallelized patch-aware attention(PPA)modules.The MBConv can increase the efficiency of feature extraction,and the DAttention module broadens the field of perception in the backbone.Besides,by introducing the PPA module in the neck,the precision of feature fusion is raised up.By testing in the SAR Ship Dataset,the results show that the precision,mAP@50 and recall rate are raised by 1.8%,1.8%,and 4.5%,respectively.Meanwhile,experiments in other four datasets are carried out and the results prove the performance of YOLOv8n-DESP are better than that of YOLOv8n.In brief,YOLOv8n-DESP is robust and generalized and has industrial application value.关键词
SAR图像/小尺度目标检测/可变形注意力/移动翻转瓶颈卷积/并行补丁感知注意力Key words
SAR image/small-scale object detection/deformable attention/mobile inverted bottleneck convolution/parallelized patch-aware attention分类
信息技术与安全科学
