{
  "abstract": "Surface defect detection in steel strips requires both accurate recognition and efficient inference under complex industrial backgrounds. Existing methods still encounter difficulties in modeling scale-varied defects, preserving fine-grained defect cues, and aligning multi-level features efficiently. To address these issues, we propose CDF-YOLO, a real-time detection framework that integrates multi-scale contextual modeling and fine-grained adaptive fusion on the YOLOv12 baseline. Specifically, a Dilated Context Pyramid Bottleneck (C2f_DCPB) is introduced to enhance contextual representation through parallel dilated branches; a Fine-grained Adaptive Fusion module (FAN_Block) is embedded into the high-resolution path to strengthen local texture and structural-detail representation; and DySample is adopted to improve content-adaptive upsampling for multi-scale feature fusion. Experiments on the NEU-DET dataset show that CDF-YOLO achieves an mAP50 of 95.4% and 101.3 FPS under the adopted test protocol, while cross-dataset evaluation on DeepPCB further indicates improved generalization performance. These results suggest that CDF-YOLO provides a favorable accuracy-efficiency trade-off on public industrial defect datasets. Nevertheless, validation on larger-scale production-line data and further optimization for edge deployment remain necessary for practical industrial application.",
  "abstract_shown": true,
  "affiliations": [
    "School of Intelligent Manufacturing and Robotics, Anqing Normal University",
    "School of Artificial Intelligence and Computing, Anqing Normal University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jiadong Dong"
    },
    {
      "affs": [
        0
      ],
      "name": "Qinghu Guo"
    },
    {
      "affs": [
        0
      ],
      "name": "Feihu Sang"
    },
    {
      "affs": [
        1
      ],
      "name": "Chunxiang Zheng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5zjd1974g1r3k.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-63696-5",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-63696-5",
  "id": "5zjd1974g1r3k",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-63696-5",
  "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"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2209|Industrial Vision Systems and Defect Detection"
    ]
  ],
  "openalex": "W7170093643",
  "pdf_url": "https://www.nature.com/articles/s41598-026-63696-5_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/5zjd1974g1r3k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:57Z",
      "native_id": "5b468fb5e636479ca981142b149566a6",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-63696-5"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Multi-scale contextual modeling and fine-grained adaptive fusion for real-time surface defect detection",
  "title_text": "Multi-scale contextual modeling and fine-grained adaptive fusion for real-time surface defect detection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:57Z",
  "url": "https://research.draconitic.com/paper/5zjd1974g1r3k",
  "venue": "Scientific Reports",
  "versions": []
}