Deep learning has driven significant progress in object detection using
Synthetic Aperture Radar (SAR) imagery. Existing methods, while achieving
promising results, often struggle to effectively integrate local and global
information, particularly direction-aware features. This paper proposes
SAR-Net, a novel framework specifically designed for global fusion of
direction-aware information in SAR object detection. SAR-Net leverages two key
innovations: the Unity Compensation Mechanism (UCM) and the Direction-aware
Attention Module (DAM). UCM facilitates the establishment of complementary
relationships among features across different scales, enabling efficient global
information fusion. Among them, Multi-scale Alignment Module (MAM) and distinct
Multi-level Fusion Module (MFM) enhance feature integration by capturing both
texture detail and semantic information. Then, Multi-feature Embedding Module
(MEM) feeds back global features into the primary branches, further improving
information transmission. Additionally, DAM, through bidirectional attention
polymerization, captures direction-aware information, effectively eliminating
background interference. Extensive experiments demonstrate the effectiveness of
SAR-Net, achieving state-of-the-art results on aircraft (SAR-AIRcraft-1.0) and
ship datasets (SSDD, HRSID), confirming its generalization capability and
robustness.

SAR-Net 是一个专门设计用于合成孔径雷达 (SAR) 目标检测中全局融合方向感知信息的创新框架，通过 Unity 补偿机制 (UCM) 和方向感知注意模块 (DAM) 实现了全局信息融合和消除背景干扰的功能。通过在飞机和船只数据集上进行实验，验证了 SAR-Net 在目标检测领域取得了最先进的结果，证实了其泛化能力和鲁棒性。