{
  "abstract": "Semantic segmentation is a pivotal computer vision task, yet pixel-level annotation is labor-intensive and costly. For ribbon-like weld seams with ambiguous boundaries, polygon-based annotation suffers severe inter-annotator variability, an issue that has received limited attention in existing model-centric research. This study proposes a centerline-based annotation strategy for Prestressed Concrete Cylinder Pipe (PCCP) weld seams to improve efficiency, consistency, and model performance. Ground truth masks are generated via the Image-Guided Centerline-to-Mask Generation algorithm using slope-adaptive kernels to convert centerlines into pixel-level masks. During training and inference, multi-directional convolutional response maps are concatenated with RGB images as six-channel inputs and evaluated on UNet, R2UNet, AttUNet, UNet++, and R2AUNet. Centerline annotation reduces interactive clicks by 63.5% and the average per-image annotation time by approximately 68%, while improving inter-annotator consistency (Cohen’s kappa: 0.913 to 0.967). Across all five architectures, mean intersection over union improves by 8.68%, with residual-recurrent networks (R2UNet and R2AUNet) gaining up to 17.32% and 13.98%, respectively. In the evaluated setting, the strategy improves annotation efficiency, inter-annotator consistency, and segmentation accuracy; its engineering feasibility was preliminarily assessed through a three-month deployment on a PCCP steel-cylinder leak detection line, during which the missed-detection rate was 0.90% (95% CI: 0.19–2.62%).",
  "abstract_shown": true,
  "affiliations": [
    "School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China",
    "Qinglong Pipe Industry Group Co., Ltd., Yinchuan 750002, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zhenyu Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Kuanmin Mao"
    },
    {
      "affs": [
        0
      ],
      "name": "Junjie Tang"
    },
    {
      "affs": [
        1
      ],
      "name": "Jiaping Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5bav0s8vnze9z.bib",
  "cited_by": 0,
  "doi": "10.3390/app162010267",
  "fulltext_url": "https://www.mdpi.com/2076-3417/16/20/10267",
  "id": "5bav0s8vnze9z",
  "issns": [
    "2076-3417"
  ],
  "landing_url": "https://doi.org/10.3390/app162010267",
  "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",
      "QC1-999"
    ],
    [
      "lcc",
      "QD1-999"
    ],
    [
      "lcc",
      "QH301-705.5"
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      "lcc",
      "T"
    ],
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      "lcc",
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    ],
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  "openalex": "W7222586342",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/5bav0s8vnze9z.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T14:21:21Z",
      "native_id": "054cd2ece92b420dbc7cc7409940a6f9",
      "source": "doaj",
      "url": "https://doi.org/10.3390/app162010267"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "lcc:QH301-705.5",
      "subfield": "general"
    },
    {
      "field": "chemistry",
      "origin": "lcc:QD1-999",
      "subfield": "general"
    },
    {
      "field": "physics",
      "origin": "lcc:QC1-999",
      "subfield": "general"
    }
  ],
  "title": "Centerline-Guided Weld Annotation for PCCP: Methods and Application Performance",
  "title_text": "Centerline-Guided Weld Annotation for PCCP: Methods and Application Performance",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:21:21Z",
  "url": "https://research.draconitic.com/paper/5bav0s8vnze9z",
  "venue": "Applied Sciences",
  "versions": []
}