{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T22:42:56Z","timestamp":1793745776936,"version":"4.6.1"},"reference-count":57,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T00:00:00Z","timestamp":1791158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/ani16193125","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T10:02:27Z","timestamp":1791194547000},"page":"3125","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DWEC-YOLO: A Multi-Behavior Detection Model for Group-Housed Pigs"],"prefix":"10.3390","volume":"16","author":[{"given":"Yan","family":"Chen","sequence":"first","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiehao","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjie","family":"Kuang","sequence":"additional","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiumei","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"189","DOI":"10.20506\/rst.33.1.2273","article-title":"Precision livestock farming technologies for welfare management in intensive livestock systems","volume":"33","author":"Berckmans","year":"2014","journal-title":"Rev. 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