  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 information 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 propose a SAR ship
detection method based on Swin Transformer 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 enhancing
the semantic information of feature maps at all levels, especially feature maps
in shallow layers. Experiments conducted on SAR ship detection dataset (SSDD)
reveal the advantage of our proposed methods.
