DASFA-Net for Accurate Ship Detection in Optical Remote Sensing Images

Ship detection in optical remote sensing imagery is pivotal for various civilian and military applications. However, due to the presence of cluttered backgrounds and the significant variations in ship scales, accurately detecting ships in complex maritime environments remains challenging. To address these challenges, we propose the Dual Attention and Scale-aware Feature Alignment Network (DASFA-Net), to effectively detect multi-scale ship targets under clutter interference. The DASFA-Net comprises two primary components: Deformable Spatial Attention Module with Channel Integration (DSAM-CI) and Bidirectional Flow Alignment Network (BiFAN). The DSAM-CI enhances discrimination between ships and distractors by jointly modeling correlations across the channel and spatial domains and applying deformable spatial attention. Concurrently, BiFAN employs a bidirectional feature flow alignment strategy to resolve spatial misalignment in feature fusion, significantly improving the quality of multi-scale feature fusion. Lastly, we design the Dynamic Alpha Complete Intersection over Union (DA-CIoU) based on prior knowledge of target characteristics to explicitly refine our detector for maritime ships. Extensive experiments on three challenging ship detection benchmarks, including HRSC2016, SCCOS, and FGSRCS have demonstrated the superiority of our DASFA.