{
  "abstract": "Ship detection from optical remote sensing images remains challenging due to cluttered sea–land backgrounds, large scale variations, low-contrast small targets, and dense ship occlusion. Existing detectors struggle to balance global modeling capability, detection precision, and computational efficiency. This paper proposes a Mamba-enhanced Real-Time Detection Transformer (RT-DETR) framework for high-precision ship detection and identification. First, a Mamba-based Deformable Spatial Attention (Mamba-DSA) backbone is proposed to integrate linear-time state-space feature modeling and deformable spatial attention, which strengthens ship-specific feature representation and suppresses background interference. Second, a Context-Guided Spatial Feature Reconstruction Feature Pyramid Network (CGRFPN) is designed to enhance multi-scale context awareness and rectangular region calibration, effectively reducing missed detections in dense ship scenes. Third, a Spatial-to-Depth Depthwise Convolution (SPD-DWConv) module is developed to enhance small-target features without introducing excessive computation overhead. Experiments on HRSC2016, NWPU VHR-10, and DOTA-v1.5 datasets show that the proposed method outperforms the baseline RT-DETR by 9.0% in precision, 16.5% in recall, 19.0% in mAP@50, and 14.1% in mAP@50:95. The proposed method achieves strong robustness and efficiency, showing high practical value for maritime monitoring, port security, and emergency response.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Qichang Zhao"
    },
    {
      "name": "Dandan Zhi"
    },
    {
      "name": "Yiquan Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/43x8vns1zj9hk.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-64122-6",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-64122-6",
  "id": "43x8vns1zj9hk",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-64122-6",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
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      "lcc",
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      "openalex",
      "1707|Advanced Neural Network Applications"
    ]
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  "openalex": "W7223110218",
  "pdf_url": "https://www.nature.com/articles/s41598-026-64122-6.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/43x8vns1zj9hk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:33Z",
      "native_id": "10.1038/s41598-026-64122-6",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-64122-6"
    },
    {
      "first_seen": "2026-10-30T13:45:53Z",
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      "url": "https://doi.org/10.1038/s41598-026-64122-6"
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  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Mamba-enhanced RT-DETR for ship detection in optical remote sensing images",
  "title_text": "Mamba-enhanced RT-DETR for ship detection in optical remote sensing images",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:53Z",
  "url": "https://research.draconitic.com/paper/43x8vns1zj9hk",
  "venue": "Scientific Reports",
  "versions": []
}