{
  "abstract": "Unmanned aerial vehicle platforms increasingly demand efficient visual anomaly detection capabilities for infrastructure inspection and environmental monitoring applications. However, deploying sophisticated deep learning models on resource-constrained embedded processors remains challenging due to computational limitations. This paper proposes a lightweight convolutional neural network architecture specifically designed for UAV onboard anomaly detection and localization. The framework centers on a Ghost-Separable Block that integrates ghost convolution, depthwise separable convolution, and coordinate attention into a single unit tailored for aerial imagery; rather than a wholly new operator, it is a deliberate combination whose value we establish empirically, trimming parameters by 26% while preserving feature discrimination. The embedded coordinate attention preserves directional spatial information essential for accurate target localization in aerial imagery. The bidirectional feature pyramid network enables effective multi-scale fusion to handle scale variations inherent in UAV-captured scenes. A dual-branch prediction head with Complete Intersection over Union loss performs simultaneous anomaly classification and spatial regression. Extensive experiments on a collected UAV aerial anomaly dataset demonstrate that the proposed method achieves 81.2% mean average precision at 0.5 IoU threshold with only 2.18 million parameters, surpassing mainstream lightweight detectors such as YOLOv8n and EfficientDet-D0: it raises mean average precision while cutting parameters by 31% and computation by 59% relative to YOLOv8n. The dual-branch head and the Complete Intersection over Union loss are adopted rather than newly proposed; the block arrangement is where the contribution lies. Beyond the self-collected data, the model preserves its accuracy-efficiency advantage on the public VisDrone benchmark, and on a Jetson Xavier NX it sustains 42 FPS natively and 67 FPS after TensorRT FP16 optimization within a 15 W power envelope, confirming real-time onboard feasibility.",
  "abstract_shown": true,
  "affiliations": [
    "State Key Laboratory of Spatial Datum",
    "RooXen Intellink Data Tech (Guangzhou) Co., Ltd",
    "School of Law and Business, Shaoyang University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zihan Jiang"
    },
    {
      "affs": [
        1
      ],
      "name": "Guiping Zhao"
    },
    {
      "affs": [
        2
      ],
      "name": "Xiaoqi Xu"
    },
    {
      "affs": [
        2
      ],
      "name": "Peilong Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9g3q1d5z6pxnk.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-68007-6",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-68007-6",
  "id": "9g3q1d5z6pxnk",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-68007-6",
  "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",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1707|Advanced Neural Network Applications"
    ]
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  "openalex": "W7204158284",
  "pdf_url": "https://www.nature.com/articles/s41598-026-68007-6_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/9g3q1d5z6pxnk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:57Z",
      "native_id": "8643df6778e74028a9604502c3bf2423",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-68007-6"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Lightweight convolutional neural network for UAV onboard visual anomaly detection and localization",
  "title_text": "Lightweight convolutional neural network for UAV onboard visual anomaly detection and localization",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:57Z",
  "url": "https://research.draconitic.com/paper/9g3q1d5z6pxnk",
  "venue": "Scientific Reports",
  "versions": []
}