A General Multiscale Pyramid Attention Module for Ship Detection in SAR Images

Compared with large-scale ships, small-scale ships occupy few pixels and have low contrast, so it poses a great challenge to detect multiscale ships in synthetic aperture radar (SAR) images. In order to improve the accuracy of multiscale ship detection in SAR images, this article designs a general multiscale pyramid attention module (MPAM), which is a plug-and-play lightweight module that can adapt to many ship detection networks. In the MPAM, a deep feature extraction submodule is first designed to use the multiscale pyramid structure to divide the feature map into different levels, extracting rich features with resolution and semantic information for multiscale ship detection. The channel multilayer attention fusion submodule and spatial multilayer attention fusion submodule are then designed to fuse the channel and spatial attention blocks on different level feature maps, which could better learn the dependent features from the channel and spatial dimensions, to enhance the feature representation. Finally, the fused feature map is input into the existing ship detection networks to obtain the detection result. Experiments on SAR datasets containing multiscale ships show that the effectiveness of the MPAM in improving the accuracy of the existing ship detection networks.