{
  "abstract": "Automated Optical Inspection equipment on Surface Mount Technology lines is tuned conservatively so that genuine defects are not missed, producing a persistent stream of false calls that a human operator must reconfirm in a secondary re-inspection step. This study reports a deep learning classifier for that decision, developed and evaluated on 34,466 images from one operating mixed-model line with an acceptable-to-defective ratio of 196 to 1. A three-stage procedure screens five architectures, compares three augmentation intensities, and sweeps twenty class weight settings under five-fold cross-validation. Two operating points are reported rather than one best model: a balanced configuration at 84.85% precision and 78.29% recall, and a high-capture configuration at 89.14% recall. Restricting evaluation to boards absent from the training partition gives 73.69% and 87.71% recall, indicating that the image-level split inflates recall by one to five percentage points. Because the classifier filters a population currently reviewed in full by hand, both configurations reduce human review volume by more than 98% on this line. An illustrative cost comparison, conditioned on an assumed accuracy of the manual re-inspection it replaces, indicates which operating point is preferred at a given ratio of released-defect cost to review cost. A secondary observation, offered as a hypothesis rather than a demonstrated result, is that per-model learning rates fixed before an augmentation study may produce apparent architecture-specific failures that are optimization artifacts.",
  "abstract_shown": true,
  "affiliations": [
    "College of Management and Economics, Tianjin University, Tianjin 300072, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Kun Tian"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhanwen Niu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2jvxcbynzf6x9.bib",
  "cited_by": 0,
  "doi": "10.3390/app162010068",
  "fulltext_url": "https://www.mdpi.com/2076-3417/16/20/10068",
  "id": "2jvxcbynzf6x9",
  "issns": [
    "2076-3417"
  ],
  "landing_url": "https://doi.org/10.3390/app162010068",
  "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"
    ],
    [
      "lcc",
      "T"
    ],
    [
      "lcc",
      "TA1-2040"
    ],
    [
      "openalex",
      "2209|Industrial Vision Systems and Defect Detection"
    ]
  ],
  "openalex": "W7221558880",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/2jvxcbynzf6x9.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T14:21:21Z",
      "native_id": "0671c9f7ff9a4cf68867fbfb23175fba",
      "source": "doaj",
      "url": "https://doi.org/10.3390/app162010068"
    }
  ],
  "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": "A Deep Learning Approach for Automated Re-Inspection of False Calls in Mixed-Production Surface Mount Lines Under Extreme Class Imbalance",
  "title_text": "A Deep Learning Approach for Automated Re-Inspection of False Calls in Mixed-Production Surface Mount Lines Under Extreme Class Imbalance",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:21:21Z",
  "url": "https://research.draconitic.com/paper/2jvxcbynzf6x9",
  "venue": "Applied Sciences",
  "versions": []
}