A Multiscale Rotation Ship Detection Network for SAR Images Based on Scale-Aware Gaussian Loss and Direction Decoupled Attention
Ship detection is one of the important application directions of synthetic aperture radar (SAR) technology. Although significant progress has been made in recent years, it still faces challenges, such as the diverse sizes of ship targets, high aspect ratios, and the cross-shaped sidelobe artifacts, caused by strong scattering in SAR imaging. To address these challenges, this article proposed a multiscale rotation ship detection network for SAR images based on scale-aware Gaussian loss and directionally decoupled attention (SGDDNet). First, a global–local perception module was designed, which employs a dual-branch mechanism in the frequency and spatial domains to acquire global context while preserving local fine-grained features. This structure enables effective multiscale feature extraction for the network. Second, a direction decoupled attention module was proposed, which performs average and maximum pooling operations along the horizontal and vertical directions, respectively. This design suppresses the feature aliasing caused by side lobe interference from targets, thereby enhancing the feature discriminability of ship targets. Furthermore, an adaptive cross-layer fusion module was proposed, employing dynamic channel weighting strategies to achieve adaptive alignment and fusion of multilevel features. In addition, a scale-aware Gaussian loss function was designed. Through the collaborative modeling of Gaussian domain measurement and parameter space constraints, it effectively alleviated the interference of the scale difference of slender ships on the regression of rotation angles. On the HRSID and SSDD+ datasets, the proposed method attained average accuracies of 91.86% and 96.89%, respectively, demonstrating superior performance over existing SAR ship detection approaches.