SAR Target Recognition Based on an Improved YOLOv5
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.
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