A General Multiscale Pyramid Attention Module for Ship Detection in SAR Images
Peng Wang, Yongkang Chen, Yi Yang, Ping Chen, Gong Zhang, Daiyin Zhu, Yongshi Jie, Cheng Jiang, Henry Leung
Abstract
Compared with large scale ships, small scale ships occupy few pixels and have low contrast, so it poses a great challenge to detect multi-scale ships in SAR images. In order to improve the accuracy of multi-scale ship detection in SAR images, this paper designs a general multi-scale pyramid attention module (MPAM), which is a plug-and-play lightweight module that can adapt to many ship detection networks. In MPAM, a deep feature extraction sub-module (DFES) is first designed to use the multi-scale pyramid structure to divide the feature map into different levels, extracting rich features with resolution and semantic information for multi-scale ship detection. The channel multi-layer attention fusion sub-module (CMAFS) and spatial multi-layer attention fusion sub-module (SMAFS) 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 multi-scale ships show that the effectiveness of MPAM in improving the accuracy of the existing ship detection networks.