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Yuhao Shen1, Baoshan Li1, Yueming Wang2
1Inner Mongolia University of Science and Technology, School of Digital and Intelligence Industry, Baotou, 014010, China; Grassland Animal Husbandry Artificial Intelligence Inner Mongolia Autonomous Region Engineering Research Center, Baotou, 014010, China.
这项研究通过整合关键点衍生的运动特征来改善奶牛的检测. 增强的YOLOv8-Pose模型和集体学习实现了超过97%的精度,用于智能的监测.
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