Multiscale Ship Detection Based On Dense Attention Pyramid Network in Sar Images

Abstract

The scales of different ships vary in synthetic aperture radar (SAR) images, especially for small scale ships, which only occupy few pixels. So ship detection methods currently face difficulties in detecting multiscale ships. A novel method for multiscale ship detection in SAR images based on Dense Attention Pyramid Network (DAPN) is proposed in this paper. It can extract multiscale and salient features by DAPN, which densely connects Convolutional Block Attention Module (CBAM) to each feature map from top to down of the pyramid network. Then the fused feature maps are fed to the detection network for multiscale ship detection. Experiments on SSDD dataset show a better performance of this method to detect multiscale ships in different scenes of SAR images.