ASFF-Det: Adaptive Space-Frequency Fusion Detector for Object Detection in SAR Images

Synthetic Aperture Radar (SAR) is essential for high-resolution Earth observation, offering all-weather capabilities. However, SAR target detection is challenging due to its coherent imaging mechanism, which introduces spatial-frequency correlations in target scattering properties. Unlike optical images, SAR images lack well-defined edges and continuous textures, making spatial feature extraction alone insufficient for robust recognition. The frequency domain provides complementary structural information, where low-frequency components capture overall shape and backscatter characteristics, while high-frequency components highlight fine details but are dominated by speckle noise. Balancing spatial and frequency features is thus crucial for accurate detection and noise suppression. To address this, we propose Adaptive Space-Frequency Fusion Detector (ASFF-Det), which integrates spatial and frequency features for SAR target detection. Our Adaptive Space-Frequency Fusion Convolution (ASFF-Conv) module integrates frequency domain transformations with spatial feature extraction, enabling adaptive multi-domain feature fusion. To mitigate high-frequency noise, our Frequency-Intensity Sorted Attention (FSA) mechanism prioritizes low-frequency structural information. Additionally, our High-Low Frequency Fusion (HLFF) module enhances multi-scale target recognition by frequency decomposition and feature enhancement. Extensive experiments on the SAR-AIRcraft-1.0, HRSID, and SSDD datasets demonstrate ASFF-Det's superiority, achieving 68.5%, 91.8%, and 96.4% mAP $_{50}$ , respectively, outperforming state-of-the-art methods. Our code is released at https://github.com/bleudeprusse/ASFF-Det