{
  "abstract": "In large-scale pig farming systems, high visual similarity among pigs, complex posture variations, and frequent interactions between individuals pose significant challenges to vision-based multi-behavior detection. To overcome these limitations, this study proposes an improved YOLO11-based pig behavior detection model, termed DWEC-YOLO, for recognizing normal behaviors (standing, lying, sitting, and feeding) and aggressive behaviors (fighting, ear biting, and tail biting). First, the C3k2-DySnake module is embedded within the backbone architecture, enabling more effective extraction of fine-grained visual details. Second, an EUCB-SC module is employed during feature aggregation and works jointly with the CoordAtt mechanism to strengthen multi-scale feature representations, thereby enhancing the network's sensitivity toward critical interaction areas. In addition, Wise-ShapeIoU is adopted as the bounding-box regression loss to refine localization optimization and produce more accurate object predictions. Comprehensive experiments show that DWEC-YOLO surpasses several representative lightweight detectors, including YOLOv5n, YOLOv7-Tiny, YOLOv8n, YOLOv10n, and YOLOv12n, in detection effectiveness. Relative to the original YOLO11 architecture, DWEC-YOLO improves precision, recall, and mAP@0.5 by 4.28, 4.46, and 2.65 percentage points, respectively, achieving 96.17%, 95.25%, and 97.57%. These outcomes demonstrate that DWEC-YOLO substantially enhances multi-behavior detection in group-housed environments, serving as a dependable tool for automated monitoring in smart livestock farming.",
  "abstract_shown": true,
  "affiliations": [
    "College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yan Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiehao Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yingjie Kuang"
    },
    {
      "affs": [
        0
      ],
      "name": "Qiumei Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/aw1j5wmra6q1w.bib",
  "cited_by": 0,
  "doi": "10.3390/ani16193125",
  "fulltext_url": "https://europepmc.org/articles/PMC13663593",
  "id": "aw1j5wmra6q1w",
  "issns": [
    "2076-2615"
  ],
  "landing_url": "https://europepmc.org/article/MED/42883094",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1110|Smart Agriculture and AI"
    ]
  ],
  "openalex": "W7220371608",
  "pdf_url": "https://europepmc.org/articles/PMC13663593?pdf=render",
  "pmcid": "PMC13663593",
  "pmid": "42883094",
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/aw1j5wmra6q1w.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:00Z",
      "native_id": "MED:42883094",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42883094"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Smart Agriculture and AI",
      "subfield": "plant-biology"
    }
  ],
  "title": "DWEC-YOLO: A Multi-Behavior Detection Model for Group-Housed Pigs.",
  "title_text": "DWEC-YOLO: A Multi-Behavior Detection Model for Group-Housed Pigs.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:00Z",
  "url": "https://research.draconitic.com/paper/aw1j5wmra6q1w",
  "venue": "Animals : an open access journal from MDPI",
  "versions": []
}