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<p>Synthetic aperture radar (SAR) ship detection in complex maritime environments remains challenging due to severe background clutter, large-scale variations, and the frequent omission of small and densely distributed vessels. To address these challenges, this paper proposes an attention-guided multi-scale feature learning framework for robust SAR ship detection. The proposed approach focuses on enhancing scale-aware feature representation, adaptive feature refinement, and cross-level feature reconstruction within a unified learning paradigm. Specifically, a multi-scale contextual modeling mechanism is introduced to capture scale-aware dependencies and improve target–background separability in cluttered scenes. An adaptive feature refinement strategy is further developed to enhance the representation of small and weak targets by strengthening shallow feature responses. In addition, an efficient feature reconstruction scheme is designed to improve cross-level feature integration during decoding, thereby enhancing localization accuracy under speckle-contaminated SAR imagery. Extensive experiments on public datasets demonstrate the effectiveness of the proposed method. On the HRSID dataset, the method achieves 94.30% mAP@0.5 and 71.53% mAP@0.5:0.95, showing clear improvements over representative detectors. On the SSDD dataset, it reaches 99.50% mAP@0.5, indicating strong generalization under different imaging conditions. Meanwhile, the proposed framework maintains high computational efficiency with over 100 FPS and low model complexity. Ablation studies further validate the effectiveness of each component and their complementary contributions. The proposed framework provides a robust and efficient solution for SAR-based maritime monitoring and can be extended to other small-object detection tasks in complex environments.</p>

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Attention guided multiscale feature learning for robust SAR ship detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月5日

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  • Xiaokang Qin,
  • Hongsheng Xu,
  • Zhaodi Wang,
  • Yike Liu,
  • Shuqiang Yang

摘要

Synthetic aperture radar (SAR) ship detection in complex maritime environments remains challenging due to severe background clutter, large-scale variations, and the frequent omission of small and densely distributed vessels. To address these challenges, this paper proposes an attention-guided multi-scale feature learning framework for robust SAR ship detection. The proposed approach focuses on enhancing scale-aware feature representation, adaptive feature refinement, and cross-level feature reconstruction within a unified learning paradigm. Specifically, a multi-scale contextual modeling mechanism is introduced to capture scale-aware dependencies and improve target–background separability in cluttered scenes. An adaptive feature refinement strategy is further developed to enhance the representation of small and weak targets by strengthening shallow feature responses. In addition, an efficient feature reconstruction scheme is designed to improve cross-level feature integration during decoding, thereby enhancing localization accuracy under speckle-contaminated SAR imagery. Extensive experiments on public datasets demonstrate the effectiveness of the proposed method. On the HRSID dataset, the method achieves 94.30% mAP@0.5 and 71.53% mAP@0.5:0.95, showing clear improvements over representative detectors. On the SSDD dataset, it reaches 99.50% mAP@0.5, indicating strong generalization under different imaging conditions. Meanwhile, the proposed framework maintains high computational efficiency with over 100 FPS and low model complexity. Ablation studies further validate the effectiveness of each component and their complementary contributions. The proposed framework provides a robust and efficient solution for SAR-based maritime monitoring and can be extended to other small-object detection tasks in complex environments.