{
  "abstract": "Bridge cracks are among the primary factors affecting structural safety and durability. Intelligent and precise detection and quantification of cracks are important in improving bridge operation and maintenance efficiency and safety assessment. Traditional manual detection methods are inefficient and highly subjective, while existing deep learning methods still have shortcomings in terms of computational overhead and detailed modelling. Therefore, this study proposes a high-precision and lightweight crack semantic segmentation network—Feather SFASwinUnet. The model introduces two new modules: Scale Fusion Attention (SFA) and Feather ConvBlock (FCB). SFA effectively fuses global semantic information and local detailed features through a dual-branch structure, which improves spatial structure modelling capabilities. Meanwhile, FCB compensates for feature distortion with extremely low overhead in skip connections, which enhances the recovery capability of fine-grained cracks. Experimental results show that, on the public dataset SDNET2018, the proposed model achieves an mIoU value of 81.25% and an mDice value of 88.57%. It significantly outperforms typical methods such as Unet, DeepLabV3+ and SwinUnet while maintaining low computational load. Furthermore, the YOLO11-BD detection algorithm is combined to achieve crack detection and segmentation under complex backgrounds, which effectively improves robustness. Lastly, the orthogonal skeleton method is used to quantitatively analyse the segmentation results. As a result, the measurement errors of crack width and length are controlled within 5%, which provides reliable technical support for bridge structural health assessment and maintenance decisions. The Feather SFASwinUnet proposed in this study provides an efficient and scalable technical approach for automatic crack detection and quantitative analysis of bridges under UAV inspection. In addition, it has high engineering application potential.",
  "abstract_shown": true,
  "affiliations": [
    "Key Laboratory of Opto-Electronic Technology and Intelligent Control, Ministry of Education, Lanzhou Jiaotong University, Lanzhou, China",
    "School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China",
    "National and Provincial Joint Engineering Laboratory of Road & Bridge Disaster Prevention and Control, Lanzhou Jiaotong University, Lanzhou, China",
    "School of Materials Science and Engineering, Southeast University, Nanjing, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xuwei Dong"
    },
    {
      "affs": [
        1
      ],
      "name": "Zhibing Yu"
    },
    {
      "affs": [
        2
      ],
      "name": "Jiashuo Yuan"
    },
    {
      "affs": [
        3
      ],
      "name": "Jinpeng Dai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/74mk17x134qxf.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0358865",
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  "id": "74mk17x134qxf",
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    "1932-6203"
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  "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": [
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      "openalex",
      "2205|Infrastructure Maintenance and Monitoring"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Attention"
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      "/Research and analysis methods/Imaging techniques"
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      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ],
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      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Attention"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358865&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/74mk17x134qxf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pone.0358865",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0358865"
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  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    }
  ],
  "title": "Automatic bridge crack detections: A lightweight segmentation model for UAV inspection",
  "title_text": "Automatic bridge crack detections: A lightweight segmentation model for UAV inspection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/74mk17x134qxf",
  "venue": "PLOS One",
  "versions": []
}