Ship Detection in SAR Images Based on Adjacent Context Guide Fusion Module and Dense Weighted Skip Connection

Abstract

Fusing features from different layers is essential to improve the ship target detection ability in the synthetic aperture radar (SAR) images. Mainstream methods usually perform simple addition or concatenation operations on adjacent feature layers without properly merging their semantic and spatial information, whereas the traditional skip connections are unable to explore sufficient information by the same scale. To address these issues, a ship detection network based on adjacent context guide fusion module and dense weighted skip connection (AFDN) in SAR images is proposed: Adjacent context guide fusion module is specially designed to capture the long-range dependencies of high-level features as weights to multiply with low-level features to fuse adjacent features more efficiently. Furthermore, the dual-path enhanced pyramid is constructed to refine and fuse multi-scale features. Finally, a dense weighted skip connection is proposed by weighted fusion of features of all sizes before the decoder to enrich the feature space. Our anchor-free AFDN outputs the spatial density map and clusters to obtain the rotatable bounding box. The experimental results indicate that the method proposed in this paper surpasses previous ship detection methods and achieves high accuracy on SSDD and AIRSARShip-1.0 datasets.