{
  "abstract": "Reliable detection of transmission-line components and defects in unmanned aerial vehicle (UAV) imagery supports preventive maintenance. Repeated downsampling, however, removes fine spatial detail, while limited interaction between feature levels hinders small-object detection. We developed YOLO11-DAL, a compact detector based on YOLO11n, to address these limitations. C3k2-LEGM integrates local structure with global context in two lower-backbone blocks. Attentional Scale Sequence Fusion (ASF) aggregates P2–P5 features, while a Dynamic Aligned Head (DAH) coordinates classification and localisation. On a seven-class UAV power-line dataset, YOLO11-DAL achieved 86.2% mAP50 and 57.7% mAP50:95. These values exceeded the YOLO11n baseline by 3.3 and 2.5% points, respectively. The parameter count decreased from 2.58 million to 2.34 million. A prototype using a ZED camera and Jetson TX2 integrated image acquisition, inference and result visualisation. End-to-end latency was not measured, so the prototype establishes functional integration rather than verified real-time performance. The results support coordinated feature fusion and task-specific prediction as a practical design for compact UAV inspection detectors.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Taotao Wu"
    },
    {
      "name": "Zhonghua Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1047yyfqg0te6.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-76269-3",
  "fulltext_url": null,
  "id": "1047yyfqg0te6",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-76269-3",
  "language": null,
  "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,
  "native_subjects": [
    [
      "openalex",
      "2210|Power Line Inspection Robots"
    ]
  ],
  "openalex": "W7222787865",
  "pdf_url": "https://www.nature.com/articles/s41598-026-76269-3_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-17",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/1047yyfqg0te6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1038/s41598-026-76269-3",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-76269-3"
    }
  ],
  "subjects": [],
  "title": "YOLO11-DAL a multi-scale fusion network for target detection in UAV-based power line inspection",
  "title_text": "YOLO11-DAL a multi-scale fusion network for target detection in UAV-based power line inspection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/1047yyfqg0te6",
  "venue": "Scientific Reports",
  "versions": []
}