Deep learning technology has been widely used in synthetic aperture radar(SAR)target detection and recognition tasks.However,due to the high noise level and unclear object edge contours in SAR images,target de-tection is challenging.In this paper,a SAR aircraft target detection model based on transformer is proposed.First,considering the characteristics of SAR images,Swin-Transformer is used as the benchmark network,which not only enhances the network's global feature extraction capability but also has lower complexity compared to Vision-Trans-former.Second,an edge detection module is proposed to enhance the edge feature extraction capability of the mod-el.Finally,a GFPN structure is proposed for feature fusion to enhance the model's multi-scale feature extraction ca-pability while ensuring that the model does not suffer from overfitting issues.
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