Sar Ship Detection Based on Swin Transformer and Feature Enhancement Feature Pyramid Network

With the booming of Convolutional Neural Networks (CNNs), CNNs such as VGG-16 and ResNet-50 widely serve as backbone in SAR ship detection. However, CNN based backbone is hard to model long-range dependencies, and causes the lack of enough high-quality semantic infor-mation in feature maps of shallow layers, which leads to poor detection performance in complicated background and small-sized ships cases. To address these problems, we pro-pose a SAR ship detection method based on Swin Trans-former and Feature Enhancement Feature Pyramid Network (FEFPN). Swin Transformer serves as backbone to model long-range dependencies and generates hierarchical features maps. FEFPN is proposed to further improve the quality of feature maps by gradually enhencing the semantic infor-mation of feature maps at all levels, especially feature maps in shallow layers. Experiments conducted on SAR ship de-tection dataset (SSDD) reveal the advantage of our pro-posed methods.