{
  "abstract": "Multi-class tree crown detection in unmanned aerial vehicle (UAV) imagery is essential for forest monitoring but remains challenged by severe target-scale variation, feature ambiguity caused by crown overlap and background interference, and unstable bounding box regression in crowded scenes. To address these coupled errors, we propose Density-Guided YOLO with Frequency Decoupling and Large Selective Kernels (DFL-YOLO), a YOLOv11-based framework that combines scale-context construction, density-conditioned feature disambiguation, and improved bounding box regression within a progressive detection process. First, structurally pruned Large Selective Kernel (LSK) modules combine cascaded depthwise convolutions with spatial selection to enrich local and long-range context along selected backbone and neck paths. Second, our Density-Guided Frequency Decoupling Branch (DFDB) predicts a Gaussian-supervised density mask from the resulting context-enhanced P4 feature and applies complementary modulation to its high- and low-frequency components, preserving boundary-sensitive responses while suppressing background interference. The modulated P4 feature is then fused with the P3 and P5 paths and passed to the multiscale detection head. Finally, Multi-Strategy Weighted Intersection over Union (MW-IoU) supervises bounding box regression by combining MPDIoU geometric constraints, the non-monotonic focusing mechanism of Wise-IoU, and task-oriented soft weights. On the Mixed NEON dataset constructed from NEON and Roboflow imagery, DFL-YOLO achieved 82.2% mAP@50 and 54.7% mAP@50:95, representing absolute improvements of 2.6% and 4.2% over the YOLOv11l baseline, respectively. After dataset-specific retraining on VisDrone2019, DFL-YOLO achieved 54.5% mAP@50 and 33.7% mAP@50:95, showing that the architecture can be trained for another dense aerial-imaging domain. In general, DFL-YOLO is a reliable method for detecting multi-class tree crowns in intricate forest environments.",
  "abstract_shown": true,
  "affiliations": [
    "ollege of Information Engineering, Sichuan Agricultural University, Ya'an, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yuling Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiong Mu"
    },
    {
      "affs": [
        0
      ],
      "name": "Binhong Zhou"
    },
    {
      "affs": [
        0
      ],
      "name": "Junhao Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Xinyue Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Chunyu Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2628c50e12tjr.bib",
  "cited_by": 0,
  "doi": "10.3389/ffgc.2026.1964673",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/ffgc.2026.1964673/full",
  "id": "2628c50e12tjr",
  "issns": [
    "2624-893X"
  ],
  "landing_url": "https://doi.org/10.3389/ffgc.2026.1964673",
  "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",
      "GE1-350"
    ],
    [
      "lcc",
      "SD1-669.5"
    ],
    [
      "openalex",
      "1707|Advanced Neural Network Applications"
    ]
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  "openalex": "W7223691157",
  "pdf_url": "https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2026.1964673/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "Frontiers Media S.A.",
  "ris": "https://research.draconitic.com/paper/2628c50e12tjr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:47:19Z",
      "native_id": "45600c68391a4311a381fa171ad29328",
      "source": "doaj",
      "url": "https://doi.org/10.3389/ffgc.2026.1964673"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "lcc:GE1-350",
      "subfield": "environmental-science"
    }
  ],
  "title": "DFL-YOLO: Density-Guided Frequency Decoupling and Large Selective Kernels improve dense multi-class tree crown detection",
  "title_text": "DFL-YOLO: Density-Guided Frequency Decoupling and Large Selective Kernels improve dense multi-class tree crown detection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:19Z",
  "url": "https://research.draconitic.com/paper/2628c50e12tjr",
  "venue": "Frontiers in Forests and Global Change",
  "versions": []
}