针对合成孔径雷达船舶检测难题，提出高效抗噪新框架
SARES-DEIM: Sparse Mixture-of-Experts Meets DETR for Robust SAR Ship Detection
- 用稀疏门控专家系统筛选特征，专注处理雷达特有噪声
- 在HRSID数据集上达到76.4% mAP50:95和93.8% mAP50
- 适合需要高精度小目标检测的遥感图像分析场景
合成孔径雷达（SAR）图像中的船舶检测面临相干斑点噪声、复杂海岸杂波及小目标普遍等挑战。传统基于光学图像设计的检测器对SAR特有退化敏感，且在空间下采样中丢失精细船体特征。为此，我们提出基于DETR范式的领域感知检测框架SARES-DEIM。核心是SARESMoE模块，通过稀疏门控机制将特征路由至专精于频率与小波的专家网络，有效抑制斑点噪声与语义杂波，同时保持高计算效率。此外，引入空间到深度增强金字塔（SDEP）结构，保留浅层高分辨率空间信息，显著提升小目标定位能力。在两个基准数据集上的大量实验表明，SARES-DEIM性能优越。尤其在具有挑战性的HRSID数据集上，mAP50:95达76.4%，mAP50达93.8%，优于主流YOLO系列及专用SAR检测器。
原文摘要 · Abstract (English)
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.
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