  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 and 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.
