论文标题
SAR船基于Swin Transformer和功能增强功能金字塔网络的检测
Sar Ship Detection based on Swin Transformer and Feature Enhancement Feature Pyramid Network
论文作者
论文摘要
随着卷积神经网络（CNN）的蓬勃发展，诸如VGG-16和Resnet-50之类的CNN广泛用作SAR船检测中的骨干。但是，基于CNN的骨干很难对远程依赖性进行建模，并且会导致缺乏浅层层的特征图中缺乏足够的高质量语义信息，从而导致在复杂的背景和小型船只中的检测性能不佳。为了解决这些问题，我们提出了一种基于Swin Transformer的SAR船检测方法，并提出了功能增强功能功能金字塔网络（FEFPN）。 Swin Transformer用作建模远程依赖性并生成分层特征图的骨架。提出了FEFPN，以进一步提高特征图的质量，通过逐渐增强各个级别的特征图的语义信息，尤其是浅层层中的特征地图。在SAR船检测数据集（SSDD）上进行的实验揭示了我们提出的方法的优势。
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.
