DSF-Net: A Dual Feature Shuffle Guided Multi-Field Fusion Network for SAR Small Ship Target Detection

Abstract

SAR images play a crucial role in ship detection across diverse scenarios due to their all-day, all-weather characteristics. However, detecting SAR ship targets poses inherent challenges due to their small sizes, complex backgrounds, and dense ship scenes. Consequently, instances of missed detection and false detection are common issues. To address these challenges, we propose the DSF-Net, a novel framework specifically designed to enhance small SAR ship detection performance. Within this framework, we introduce the Pixel-wise Shuffle Attention module (PWSA) as a pivotal step to strengthen the feature extraction capability. To enhance long-range dependencies and facilitate information communication between channels, we propose a Non-Local Shuffle Attention (NLSA) module. Moreover, NLSA ensures the stability of the feature transfer structure and effectively addresses the issue of missed detection for small-sized targets. Secondly, we introduce a novel Triple Receptive Field-Spatial Pyramid Pooling (TRF-SPP) module designed to mitigate the issue of false detection in complex scenes stemming from inadequate contextual information. Lastly, we propose the R-tradeoff loss to augment the detection capability for small targets, expedite training convergence, and fortify resistance against false detection. Quantitative validation and qualitative visualization experiments are conducted to substantiate the proposed assumption of structural stability and evaluate the effectiveness of the proposed modules. On the LS-SSDDv1.0 dataset, the mAP50−95 demonstrates a remarkable improvement of 8.5% compared to the baseline model. The F1 score exhibits a notable enhancement of 6.9%, surpassing the performance of advanced target detection methods such as YOLO V8.