A Novel Anchor-Free Detector Using Global Context-Guide Feature Balance Pyramid and United Attention for SAR Ship Detection

Most synthetic aperture radar (SAR) ship detectors based on convolutional neural networks (CNNs) needed preset anchor boxes to object classification and bounding box coordinate regression. However, the sparsity and unbalanced distribution of ships in SAR images mean that most anchor boxes are redundant. Thus, the anchor settings directly affect the performance and generalization ability of the detector. In addition, a variety in ship scales and the substantial interference of inshore backgrounds bring significant challenges to the SAR ship detector's performance improvement. In this letter, a novel anchor-free-based detector, named feature balance and united attention (FBUA-Net), is proposed. We adopt a keypoint-based strategy to predict bounding boxes to eliminate the influence of anchors. Besides, we propose a global context-guided feature balanced pyramid (GC-FBP), which balances the semantic information at different levels of the feature pyramid by aggregation and averaging and uses the global context module (GCM) to learn global contextual information to construct long-range dependencies between ship targets and the background. Considering the interference of scattering noise to the detector, a united attention module (UAM) is designed to reduce the interference of surrounding noise by focusing on the spatial shape and scale size of ship targets in both the spatial and scale domains. Experimental results on the SAR ship detection dataset (SSDD) and high-resolution SAR images dataset (HRSID) datasets show that our detector achieves state-of-the-art (SOTA) performance.