{
  "abstract": "The accurate localization of charging holes is an indispensable perception step for the automatic charging system of electric vehicles, since the success of subsequent charging-gun insertion depends directly on the precise estimation of the geometric centers of the holes on the vehicle charging interface. This paper presents a lightweight machine-vision framework for charging hole localization based on contour-driven detection and geometric verification. The proposed framework integrates image filtering, RGB-to-HSL color-space conversion, parameterized HSL adjustment, two-stage contour detection, minimum enclosing circle fitting, and Hough circle verification into a deterministic pipeline. The framework is interpretable, does not require a learning-based training process, and can be deployed on conventional CPU-based computing resources. The image data used in this study were acquired by a monocular RGB camera mounted on the charging robot end-effector, in the same sensing configuration in which the system is intended to operate. A total of 80 images were collected from six vehicles equipped with the same five-hole charging interface, under realistic variations of illumination, viewing angle, reflective interference, and background complexity. Detailed parameter selection rationale, a parameter perturbation analysis, and a condition-specific ablation analysis are reported. On an Intel Core i7-12700 CPU with 16 GB RAM and no GPU acceleration, the proposed framework achieves an overall mean localization error of 4.2 pixels and a 96.25% detection success rate, with an average per-image processing time of approximately 60 ms. The results demonstrate that the proposed framework provides accurate, robust, and engineering-deployable charging-hole localization for the target charging interface.",
  "abstract_shown": true,
  "affiliations": [
    "Nanjing University of Science and Technology Zijin College, Nanjing, China.",
    "NARI Technology Development Co. Ltd, Nanjing, China.",
    "State Grid Electric Power Research Institute, Nanjing, China."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Wei Luo",
      "orcid": "0009-0008-1093-388X"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Qiantao Huo"
    },
    {
      "affs": [
        0
      ],
      "name": "Xin Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/45x6z6jtqw759.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0359570",
  "fulltext_url": "https://europepmc.org/articles/PMC13690247",
  "id": "45x6z6jtqw759",
  "issns": [
    "1932-6203"
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  "landing_url": "https://doi.org/10.1371/journal.pone.0359570",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Perception/Sensory perception/Vision"
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  "pdf_url": "https://europepmc.org/articles/PMC13690247?pdf=render",
  "pmcid": "PMC13690247",
  "pmid": "42911947",
  "published": "2026-10-22",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/45x6z6jtqw759.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:23Z",
      "native_id": "10.1371/journal.pone.0359570",
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      "url": "https://doi.org/10.1371/journal.pone.0359570"
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      "native_id": "MED:42911947",
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  "subjects": [
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      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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      "field": "mathematics",
      "origin": "plos:geometry",
      "subfield": "geometry-topology"
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  "title": "A machine-vision framework for electric vehicle charging hole localization based on contour-driven detection and geometric verification",
  "title_text": "A machine-vision framework for electric vehicle charging hole localization based on contour-driven detection and geometric verification",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:10Z",
  "url": "https://research.draconitic.com/paper/45x6z6jtqw759",
  "venue": "PLOS One",
  "versions": []
}