{
  "abstract": "Continuous, reliable monitoring of geographically dispersed oil and gas production facilities is essential for accident prevention, asset integrity, and regulatory compliance, yet ground-based inspection is labour-intensive and temporally sparse. Unmanned aerial vehicles (UAVs) offer on-demand aerial coverage, but automated interpretation of production-site imagery is hindered by extreme target-scale variation, irregular equipment geometries, and the limited compute available on airborne platforms. This paper presents an integrated lightweight detection-and-tracking framework tailored to oil and gas field surveillance. The detector augments YOLOv8s with deformable C2f (DCNv2) modules for geometry-adaptive feature extraction, a progressive feature pyramid (AFPN) that suppresses cross-scale semantic conflict, a parameter-shared detail-enhanced head (LSDECD-Head), a Focaler-GIoU regression loss, and LAMP structured pruning for embedded deployment. For dynamic situational awareness, an improved ByteTrack introduces a spatial–appearance similarity matrix encoding operational-state cues and an acceleration-aware Kalman correction. On VisDrone2019, UAVDT, and a self-constructed oil-and-gas dataset (OGF-UAV), the method attains 44.1%, 57.6%, and 56.3% mAP@0.5 with only 7.8 M parameters at 78 FPS, and reduces tracking identity switches by up to 43.2%. The pruned model runs at 18.3 FPS on an NVIDIA Jetson Orin NX 16 GB under TensorRT FP16, confirming practicality for real-time field deployment.",
  "abstract_shown": true,
  "affiliations": [
    "Digital Intelligent Technology Subsidiary of Southwest Oil and Gas Field, Chengdu, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jianhua Gong"
    },
    {
      "name": "Yifu Wang"
    },
    {
      "name": "Jun Zhang"
    },
    {
      "name": "Defei Du"
    },
    {
      "name": "Yang Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/atmny30vr6d3s.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0358757",
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1707|Advanced Neural Network Applications"
    ],
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      "plos",
      "/Biology and life sciences/Anatomy/Head"
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      "/Biology and life sciences/Anatomy/Neck"
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      "plos",
      "/Biology and life sciences/Biochemistry/Lipids/Oils"
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      "/Physical sciences/Physics/Classical mechanics/Acceleration"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358757&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-25",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/atmny30vr6d3s.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pone.0358757",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0358757"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "Lightweight UAV-based object detection and tracking for intelligent oil and gas field safety monitoring",
  "title_text": "Lightweight UAV-based object detection and tracking for intelligent oil and gas field safety monitoring",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/atmny30vr6d3s",
  "venue": "PLOS One",
  "versions": []
}