{
  "abstract": "Automated visual inspection of aero-engine blades is central to aviation safety, yet it is challenged by damage that is small and low-contrast against a strongly textured metal surface, and by the need for models that are light enough for edge deployment. This paper presents a lightweight vision-based framework for automated blade damage inspection. Rather than assembling independent detection modules, the framework is organized as a three-stage inspection pipeline: (1) a multi-scale feature-discrimination stage (MSBlock) captures defects across a wide size range at negligible computational cost; (2) a texture-suppression stage (TSA) filters high-frequency machining-background noise through product-based channel–spatial attention, exposing genuine defects; and (3) an efficient-prediction stage (Detect_Efficient) fuses cross-scale features and recalibrates confidence with roughly half the parameters of a standard head. On a self-built engine-blade dataset, the framework improves recall, F1-score, and mAP@0.5 by 4.8, 1.9, and 0.8 percentage points over YOLOv11n (reaching 92.0%, 91.6%, and 94.2%), while cutting parameters and computation by 50.0% and 58.7% and reaching 527 FPS. It also outperforms YOLOv8n, YOLOv9t, and YOLOv10n in recall and F1-score, confirming its suitability for real-time edge-based blade inspection.",
  "abstract_shown": true,
  "affiliations": [
    "School of Aviation, Zhejiang Institute of Communications, Hangzhou, Zhejiang, China",
    "School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China",
    "Loongair(Hangzhou) Aviation Maintenance Engineering Co., Ltd., Hangzhou, Zhejiang, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xiaobo Ning"
    },
    {
      "affs": [
        1
      ],
      "name": "Hang Zhao"
    },
    {
      "affs": [
        2
      ],
      "name": "Kang Zeng"
    },
    {
      "name": "Zhengwei Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/61sah3kv0bmd2.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0362006",
  "fulltext_url": null,
  "id": "61sah3kv0bmd2",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0362006",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "plos",
      "/Biology and life sciences/Anatomy/Head"
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      "plos",
      "/Biology and life sciences/Anatomy/Neck"
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      "plos",
      "/Computer and information sciences/Network analysis/Centrality"
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      "/Engineering and technology/Industrial engineering/Quality control/Visual inspection"
    ],
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      "/Engineering and technology/Signal processing/Signal to noise ratio"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Head"
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    [
      "plos",
      "/Medicine and health sciences/Anatomy/Neck"
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      "plos",
      "/Medicine and health sciences/Ophthalmology/Visual impairments"
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  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/61sah3kv0bmd2.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T12:20:42Z",
      "native_id": "10.1371/journal.pone.0362006",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0362006"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "A lightweight vision-based framework for automated aero-engine blade damage inspection",
  "title_text": "A lightweight vision-based framework for automated aero-engine blade damage inspection",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:20:24Z",
  "url": "https://research.draconitic.com/paper/61sah3kv0bmd2",
  "venue": "PLOS One",
  "versions": []
}