{
  "abstract": "The appearance defects of cigars can significantly compromise their overall quality, with detection currently relying mainly on manual inspection, a process that is time-consuming and inefficient. The ASC-YOLOv8n model has been proposed for high-precision automated defect detection in full-leaf handmade cigars, targeting defects such as green spots, holes, breaks, and tail breaks during production. This model incorporates several key improvements: it integrates an ASC (Adaptive Spatial Context) module to provide a more hierarchical receptive field, enhancing its ability to detect intricate defect patterns; replaces the conventional C2f module with a more advanced Fusion module, which combines multiple feature maps to improve feature extraction capabilities; and employs a novel WIoU (Weighted Intersection over Union) localization loss function, significantly refining defect localization precision. Experimental results show that the ASC-YOLOv8n model achieves a performance boost, with the mean average precision at an IoU threshold of 0.5 (mAP@0.5) increasing by 1.7% points to 94.00%, outperforming the baseline YOLOv8n model. This demonstrates the model’s effectiveness in accurately identifying critical cigar defects, making it a reliable and robust solution for intelligent cigar inspection, and contributing to enhanced quality control in cigar production.",
  "abstract_shown": true,
  "affiliations": [
    "Zhengzhou Tobacco Research Institute of CNTC",
    "China Tobacco Sichuan Industrial Co., Ltd.",
    "China Tobacco Shandong Industrial Co., Ltd."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
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        0
      ],
      "name": "Xinan Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Tao Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Xinyi Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Xi Hu"
    },
    {
      "affs": [
        1
      ],
      "name": "Jing Gao"
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    {
      "affs": [
        2
      ],
      "name": "Xiaolong Yi"
    },
    {
      "affs": [
        2
      ],
      "name": "Peng Guo"
    },
    {
      "affs": [
        1
      ],
      "name": "Rui Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Wu Wen"
    },
    {
      "affs": [
        1
      ],
      "name": "Rongya Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Wenkui Zhu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aj84g1xttmrb4.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-62533-z",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-62533-z",
  "id": "aj84g1xttmrb4",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-62533-z",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
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      "lcc",
      "R"
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    [
      "openalex",
      "2209|Industrial Vision Systems and Defect Detection"
    ]
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  "openalex": "W7170177136",
  "pdf_url": "https://www.nature.com/articles/s41598-026-62533-z_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/aj84g1xttmrb4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:48Z",
      "native_id": "ba52714671404e97bdb5f4dcf5431efc",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-62533-z"
    }
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  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "ASC-YOLOv8n: Enhanced multi-scale feature fusion for accurate detection of four cigar appearance defects",
  "title_text": "ASC-YOLOv8n: Enhanced multi-scale feature fusion for accurate detection of four cigar appearance defects",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:48Z",
  "url": "https://research.draconitic.com/paper/aj84g1xttmrb4",
  "venue": "Scientific Reports",
  "versions": []
}