An Enhanced Shuffle Attention with Context Decoupling Head with Wise IoU Loss for SAR Ship Detection

Abstract

Synthetic Aperture Radar (SAR) imagery is widely utilized in military and civilian applications. Recent deep learning advancements have led to improved ship detection algorithms, enhancing accuracy and speed over traditional Constant False-Alarm Rate (CFAR) methods. However, challenges remain with complex backgrounds and multi-scale ship targets amidst significant interference. This paper introduces a novel method that features a context-based decoupled head, leveraging positioning and semantic information, and incorporates shuffle attention to enhance feature map interpretation. Additionally, we propose a new loss function with a dynamic non-monotonic focus mechanism to tackle these issues. Experimental results on the HRSID and SAR-Ship-Dataset demonstrate that our approach significantly improves detection performance over the original YOLOv5 algorithm and other existing methods.