文献类型： 外文期刊

作者： Liang, Ziyuan ¹ ; Xu, Aijun ¹ ; Ye, Junhua ² ; Zhou, Suyin ¹ ; Weng, Xiaoxing ³ ; Bao, Sian ⁴ ;

作者机构： 1.Zhejiang Agr & Forestry Univ, Sch Math & Comp Sci, Hangzhou 311300, Peoples R China

2.Zhejiang Agr & Forestry Univ, Sch Environm & Resource Sci, Hangzhou 311300, Peoples R China

3.Zhejiang Acad Agr Machinery, Jinhua 321000, Peoples R China

4.Zhongrun Agr & Anim Husb Technol Zhejiang Co Ltd, Jinhua 321000, Peoples R China

关键词： pig; movement monitoring; YOLOv8; agglomerative clustering; spatial moment

期刊名称：ANIMALS （ 影响因子：2.7； 五年影响因子：3.2 ）

ISSN： 2076-2615

年卷期： 2024 年 14 卷 20 期

页码：

收录情况： SCI

摘要： Continuous movement monitoring helps quickly identify pig abnormalities, enabling immediate action to enhance pig welfare. However, continuous and precise monitoring of daily pig movement on farms remains challenging. We present an approach to automatically and precisely monitor the movement of group-housed pigs. The instance segmentation model YOLOv8m-seg was applied to detect the presence of pigs. We then applied a spatial moment algorithm to quantitatively summarize each detected pig's contour as a corresponding center point. The agglomerative clustering (AC) algorithm was subsequently used to gather the pig center points of a single frame into one point representing the group-housed pigs' position, and the movement volume was obtained by calculating the displacements of the clustered group-housed pigs' center points of consecutive frames. We employed the method to monitor the movement of group-housed pigs from April to July 2023; more than 1500 h of top-down pig videos were recorded by a surveillance camera. The F1 scores of the trained YOLOv8m-seg model during training were greater than 90% across most confidence levels, and the model achieved an mAP50-95 of 0.96. The AC algorithm performs with an average extraction time of less than 1 millisecond; this method can run efficiently on commodity hardware.