{
  "abstract": "We present a deep learning detection method capable of effectively identifying planetary nebulae (PNe) on IPHAS and VPHAS+ surveys. Our detection pipeline is based on the YOLO architecture and is adaptable to other datasets. Using the HiPS system, we preprocess and augment the r , i , and H α bands to produce two red, green, and blue (RGB) image datasets: (1) 640 × 640 px images at full resolution, and (2) 1 280 × 1 280 px images designed to capture extended objects up to 0.2 deg. Images of ∼2600 PNe are labelled using the HASH database. The loss function and evaluation metrics are adapted to the diffuse and low-contrast nature of PNe; a very low intersection over union (IoU) threshold (1%) is used. A k -fold cross validation shows that the YOLOv12m model achieves an average precision of 0.979 on the first dataset. At a confidence threshold of 0.15, it achieves a precision rate of 98.7% and a recall rate of 97.4%. We deployed our system (on 1280 × 1280 images) on the full surveys’ coverage (∼3755 deg 2 ), and present a preliminary analysis of the detection results. The system is robust against artifacts such as CCD corner defects and HiPS tile joints. Our convolutional-neural-network-based model achieved excellent results on the test set, comparable to—or exceeding—that of a recent Swin Transformer approach. However, a challenge persists when applying deep learning models to extensive and densely populated Galactic fields: the development of an automated system that can effectively discriminate PNe from mimics that human experts are able to distinguish (e.g., H II regions). We outline possible improvements for future work.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "Titouan Sérandour"
    },
    {
      "name": "Anne-Laure Méalier"
    },
    {
      "name": "Bruno Colicchio"
    },
    {
      "name": "Dominique Daniel"
    },
    {
      "name": "Philippe Gilberton"
    },
    {
      "name": "Philippe Oros"
    },
    {
      "name": "Thomas Boch"
    },
    {
      "name": "Djaffar Ould Abdeslam"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2j30hx48mwy54.bib",
  "cited_by": 0,
  "doi": "10.3847/1538-3881/aea417",
  "fulltext_url": null,
  "id": "2j30hx48mwy54",
  "issns": [
    "0004-6256",
    "1538-3881"
  ],
  "landing_url": "https://hal.science/hal-05770740v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
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      "openalex",
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  "pdf_url": "https://hal.science/hal-05770740/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": "American Astronomical Society",
  "ris": "https://research.draconitic.com/paper/2j30hx48mwy54.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:46Z",
      "native_id": "hal-05770740",
      "source": "hal",
      "url": "https://hal.science/hal-05770740v1"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "hal:2.phys.astr.im",
      "subfield": "instrumentation-methods"
    },
    {
      "field": "physics",
      "origin": "hal:0.phys",
      "subfield": "general"
    }
  ],
  "title": "Identifying Planetary Nebulae Candidates from IPHAS and VPHAS+ Surveys via YOLO Deep Learning Models",
  "title_text": "Identifying Planetary Nebulae Candidates from IPHAS and VPHAS+ Surveys via YOLO Deep Learning Models",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:46Z",
  "url": "https://research.draconitic.com/paper/2j30hx48mwy54",
  "venue": "The Astronomical Journal",
  "versions": [
    {
      "doi": "10.48550/arxiv.2610.19760",
      "pid": "1m8096mpss42c",
      "relation": "preprint",
      "title": "Identifying Planetary Nebulae Candidates from IPHAS and VPHAS+ Surveys via YOLO Deep Learning Models",
      "venue": "arXiv"
    }
  ]
}