{
  "abstract": "This study presents a two-stage framework for binary plasterboard quality inspection that combines edge-region-of-interest (ROI) segmentation with lightweight classification. A production-line dataset of 5,557 images was acquired using an operator-triggered single-board-computer imaging system. On the 916-image manually masked subset, TransAttUNet produced higher ROI-overlap and boundary metrics than U-Net. The proposed MobileNetV2-MobileViT classifier contains 3.91 million parameters and was evaluated on a held-out set of 838 images, achieving 97.37% overall accuracy and 96.99% balanced accuracy. It achieved the highest accuracy among the evaluated transfer-learning baselines under the shared test protocol. Raspberry Pi 4 was used for image acquisition only; on-device inference, latency, memory, and power were not evaluated. Accordingly, the system should be interpreted as a compact, deployment-oriented framework that requires device-level validation before claims of real-time operation can be made.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Mustafa Teke",
      "orcid": "0000-0002-7262-4918"
    },
    {
      "name": "Mehmet Demir"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3js12tkcrwn8q.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-76925-8",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-76925-8",
  "id": "3js12tkcrwn8q",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-76925-8",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2209|Industrial Vision Systems and Defect Detection"
    ]
  ],
  "openalex": "W7223029478",
  "pdf_url": "https://www.nature.com/articles/s41598-026-76925-8.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/3js12tkcrwn8q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:31Z",
      "native_id": "10.1038/s41598-026-76925-8",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-76925-8"
    },
    {
      "first_seen": "2026-10-30T13:45:54Z",
      "native_id": "a9fad09cd5de4b8ebd7432fa43a467cd",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-76925-8"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Deep learning based edge segmentation and classification for plasterboard defect detection",
  "title_text": "Deep learning based edge segmentation and classification for plasterboard defect detection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:54Z",
  "url": "https://research.draconitic.com/paper/3js12tkcrwn8q",
  "venue": "Scientific Reports",
  "versions": []
}