现代雷达2024，Vol.46Issue(2)：138-145,8.DOI:10.16592/j.cnki.1004-7859.2024.02.018
基于改进YOLOv5卷积神经网络的SAR图像目标识别
SAR Target Recognition Based on an Improved YOLOv5
摘要
Abstract
An improved YOLOv5 network is proposed in this paper and is applied in SAR image target recognition.In order to opti-mize the performance of the network,three improvements are made as following.Firstly,width ratio and height ratio are used as the distance metric between labeled boxes,and k-means clustering method are used to generate a priori anchor box as the initial value of box size for prediction box optimization.Secondly,the regression loss function is improved in that CIoU is replaced by SIoU to improve the localization accuracy for densely distributed targets.Finally,the confidence loss function is improved in that binary cross entropy is replaced by Focal Loss to improve the target recognition accuracy in complex backgrounds.In this paper,based on the MS AR dataset,YOLOv3 and conventional YOLOv5 are selected as the comparison networks,and a large number of SAR image target recognition experiments are conducted.The experiment results show that the improved YOLOv5 network has higher recognition accuracy,recall rate,F1,AP and mAP for all types of targets compared with the two comparison networks.关键词
卷积神经网络/YOLOv5网络/SAR图像/目标识别Key words
CNN/YOLOv5/SAR images/target recognition分类
信息技术与安全科学引用本文复制引用
曾祥书,黄一飞,蒋忠进..基于改进YOLOv5卷积神经网络的SAR图像目标识别[J].现代雷达,2024,46(2):138-145,8.基金项目
国家自然科学基金资助项目(61890544,91748106) （61890544,91748106）
