To solve the problem of low detection rate of small targets and high false alarm rate due to the small size of aircraft in SAR images,an improved method based on YOLOv5 is proposed.First,the K-means clustering algorithm is used to optimize the anchor frame for the size of the small aircraft target,and the Swin Transformer module is integrated into the backbone network.At the same time,the multi-scale feature fusion mechanism of adaptive learning weights and the Global Attention Mechanism (GAM) are introduced to make the network span the space channel dimension and amplify the global dimension interaction,so as to improve the models ability to capture information in different dimensions.A small target detection layer is added to improve the networks ability to detect small aircraft targets in SAR images.The experimental results show that,compared with the original YOLOv5 method,the improved method has stronger feature extraction ability and higher detection accuracy in the detection of small-size aircraft targets in SAR images.