Sar Ship Detection based on Swin Transformer and Feature Enhancement Feature Pyramid Network
This addresses detection challenges in SAR imagery for applications like maritime surveillance, but is incremental as it combines existing components (Swin Transformer and FEFPN) for a specific domain.
The paper tackles poor SAR ship detection performance in complex backgrounds and for small ships by proposing a method using Swin Transformer as backbone to model long-range dependencies and a Feature Enhancement Feature Pyramid Network (FEFPN) to improve semantic information in feature maps, achieving improved results on the SSDD dataset.
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.