Modulation and Perturbation in Frequency Domain for SAR Ship Detection

Synthetic Aperture Radar (SAR) has unique advantages in ship monitoring at sea due to its all-weather imaging capability. However, its unique imaging mechanism presents two major challenges. First, speckle noise in the frequency domain reduces the contrast between the target and the background. Second, side-lobe scattering blurs the ship outline, especially in nearshore complex scenes, and strong scattering characteristics make it difficult to separate the target from the background. The above two challenges significantly limit the performance of tailored CNN-based detection models in optical images when applied directly to SAR images. To address these challenges, this paper proposes a modulation and perturbation mechanism in the frequency domain based on a lightweight CNN detector. Specifically, the wavelet transform is firstly used to extract high-frequency features in different directions, and feature expression is dynamically adjusted according to the global statistical information to realize the selective enhancement of the ship edge and detail information. In terms of frequency-domain perturbation, a perturbation mechanism guided by frequency-domain weight is introduced to effectively suppress background interference while maintaining key target characteristics, which improves the robustness of the model in complex scenes. Extensive experiments on four widely adopted benchmark datasets, namely LS-SSDD-v1.0, SSDD, SAR-Ship-Dataset, and AIR-SARShip-2.0, demonstrate that our FMP-Net significantly outperforms 18 existing state-of-the-art methods, especially in complex nearshore scenes and sea surface interference scenes.