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<p>Synthetic Aperture Radar (SAR) enables all-weather and day-and-night maritime monitoring, yet accurate ship detection remains challenging due to multiplicative speckle, sea/near-shore clutter, and large intra-class appearance variation that can destabilize multi-scale feature fusion and degrade high-IoU localization. In this work, we present <b>LG-RSD-AttnFPN</b>, a practical enhancement to a standard one-stage PAN–FPN detector that addresses SAR-specific nuisance factors from both representation and supervision perspectives. First, we introduce <b>AttnFPN</b>, a lightweight channel-recalibration module inserted after pyramid fusion to suppress clutter-dominant activations and improve the conditioning of multi-scale features, leading to more stable classification and tighter bounding-box regression in complex coastal scenes. Second, we propose <b>LG-RSD</b> (Local–Global Region Self-Distillation), a <i>training-only</i> regularizer that enforces region-level semantic consistency by aligning RoI-pooled embeddings from dense pyramid features (local student view) with crop-based holistic embeddings computed by a frozen teacher (global view) in the same augmented coordinate system. LG-RSD is fully removed during inference, introducing zero test-time overhead. Extensive experiments on two representative SAR ship detection benchmarks, HRSID and SSDD, under a strict COCO-style protocol demonstrate that our method achieves state-of-the-art performance with consistent gains across AP, AP<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(_{75}\)</EquationSource> </InlineEquation>, and scale-specific metrics, confirming improved localization tightness and robustness to clutter and speckle.</p>

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LG-RSD: local–global region self-distillation for robust SAR ship detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月24日

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  • Zhe Jing,
  • Zhengguo Yan

摘要

Synthetic Aperture Radar (SAR) enables all-weather and day-and-night maritime monitoring, yet accurate ship detection remains challenging due to multiplicative speckle, sea/near-shore clutter, and large intra-class appearance variation that can destabilize multi-scale feature fusion and degrade high-IoU localization. In this work, we present LG-RSD-AttnFPN, a practical enhancement to a standard one-stage PAN–FPN detector that addresses SAR-specific nuisance factors from both representation and supervision perspectives. First, we introduce AttnFPN, a lightweight channel-recalibration module inserted after pyramid fusion to suppress clutter-dominant activations and improve the conditioning of multi-scale features, leading to more stable classification and tighter bounding-box regression in complex coastal scenes. Second, we propose LG-RSD (Local–Global Region Self-Distillation), a training-only regularizer that enforces region-level semantic consistency by aligning RoI-pooled embeddings from dense pyramid features (local student view) with crop-based holistic embeddings computed by a frozen teacher (global view) in the same augmented coordinate system. LG-RSD is fully removed during inference, introducing zero test-time overhead. Extensive experiments on two representative SAR ship detection benchmarks, HRSID and SSDD, under a strict COCO-style protocol demonstrate that our method achieves state-of-the-art performance with consistent gains across AP, AP \(_{75}\) , and scale-specific metrics, confirming improved localization tightness and robustness to clutter and speckle.