Ship detection in Synthetic Aperture Radar (SAR) imagery is fundamentally challenged by inherent coherent speckle noise, complex coastal clutter, and the prevalence of small-scale targets. Conventional detectors, primarily designed for optical imagery, often exhibit limited robustness against SAR-specific degradation and suffer from the loss of fine-grained ship signatures during spatial downsampling. To address these limitations, we propose SARES-DEIM, a domain-aware detection framework grounded in the DEtection TRansformer (DETR) paradigm. Central to our approach is SARESMoE (SAR-aware Expert Selection Mixture-of-Experts), a module leveraging a sparse gating mechanism to selectively route features toward specialized frequency and wavelet experts. This sparsely-activated architecture effectively filters speckle noise and semantic clutter while maintaining high computational efficiency. Furthermore, we introduce the Space-to-Depth Enhancement Pyramid (SDEP) neck to preserve high-resolution spatial cues from shallow stages, significantly improving the localization of small targets. Extensive experiments on two benchmark datasets demonstrate the superiority of SARES-DEIM. Notably, on the challenging HRSID dataset, our model achieves a mAP50:95 of 76.4% and a mAP50 of 93.8%, outperforming state-of-the-art YOLO-series and specialized SAR detectors.

翻译：合成孔径雷达（SAR）图像中的舰船检测面临固有相干斑噪声、复杂海岸杂波及小尺度目标普遍存在的根本性挑战。传统检测器主要面向光学图像设计，对SAR特有的退化现象鲁棒性有限，且在下采样过程中易丢失细粒度舰船特征。为解决上述问题，本文提出SARES-DEIM——一种基于检测变换器（DETR）范式的领域感知检测框架。该框架的核心是SARESMoE（SAR感知专家选择混合专家）模块，该模块通过稀疏门控机制将特征选择性地路由至专用频率专家与小波专家。这种稀疏激活架构在保持高计算效率的同时，有效滤除了斑噪和语义杂波。此外，我们引入空间转深度增强金字塔（SDEP）颈部结构以保留浅层高分辨率空间线索，显著提升小目标定位精度。在两个基准数据集上的广泛实验证明了SARES-DEIM的优越性。特别地，在具有挑战性的HRSID数据集中，我们的模型实现了76.4%的mAP50:95和93.8%的mAP50，超越了当前最先进的YOLO系列及专用SAR检测器。