智慧农业（中英文）2026，Vol.8Issue(4)：127-147,21.DOI:10.12133/j.smartag.SA202601021
DC-YOLO:应对遮挡和光照变化的湖羊行为检测
DC-YOLO:A Behavior Detection Model for Hu Sheep Addressing Occlusion and Illumination Variations
摘要
Abstract
[Objective]In the livestock industry,animal behavior serves as a critical indicator for evaluating physiological health and providing environmental early warning.To address the challenges of severe multi-object occlusion,complex il-lumination,and class imbalance in intensive farming,a detail-contextual attention-YOLO(DC-YOLO)model was devel-oped for the 24/7 automatic detection of daily behaviors in housed Hu sheep,thereby facilitating precision management.[Methods]For severe multi-object occlusion,an adaptive detail-contextual attention(DCAttention)mechanism was intro-duced to construct the DCAC3K2 module,and bounding box similarity soft-NMS(BS-NMS)was adopted to prevent in-correct removal of occluded bounding boxes.For complex illumination,a Light-Encoder was pre-trained on illumination conditions and its parameters were transferred to DC-YOLO.For class imbalance,a comprehensive classification-quality focal loss was designed to adaptively increase loss weights for occluded and minority-class samples.The dataset,collected through 24/7 surveillance at Hu sheep farms,comprised 505 annotated images depicting 5 daily behaviors:drinking,eat-ing,lying,licking,and standing.[Results and Discussions]DC-YOLO achieved mAP@50 of 91.4%,improving by 7.8 percentage points over YOLOv12.Moreover,DC-YOLO had 2.29 M parameters,an 8.74%reduction compared to YO-LOv12.On CPU,the inference time of DC-YOLO was reduced to 115.5 ms and the frame rate increased to 8.50 f/s,corre-sponding to improvements of 33.2%and 48.9%,respectively.[Conclusions]Experimental results demonstrate that DC-YOLO effectively mitigates detection challenges caused by severe occlusion,complex illumination,and class imbalance while maintaining high inference efficiency.Consequently,it provides effective technical support for tracking and analyz-ing behaviors in intensive Hu sheep farming.关键词
集约化养殖/行为检测/湖羊行为/注意力机制/YOLO/BS-NMSKey words
Intensive livestock farming/Behavior detection/Hu sheep behavior/Attention Mechanism/YOLO/BS-NMS分类
信息技术与安全科学引用本文复制引用
李夏溪,冀荣华,常宏瑞,张所向..DC-YOLO:应对遮挡和光照变化的湖羊行为检测[J].智慧农业（中英文）,2026,8(4):127-147,21.基金项目
National Key Research and Development Program of China(2022YFD1301104) 国家重点研发计划项目(2022YFD1301104) （2022YFD1301104）
