Cattle Posture Estimation Based on YOLO Algorithm

Abstract

Abstract: Cattle posture estimation plays a crucial role in analyzing cattle behavior and evaluating cattle health. To address the problem that cattle cannot be monitored around the clock and corresponding behavioral information cannot be obtained in a timely manner, the YOLO algorithm is used to study cattle posture and establish a cattle posture estimation model for identifying cattle and extracting their skeleton structure. The test results show that compared with YOLOv7-w6-pose, the YOLOv8n-pose cattle posture estimation model has increased accuracy, recall rate, and mean average accuracy at Loks=0.50（mAP0.50） by 4.7%, 3.0%, and 2.7%,
respectively. The model parameter and computational complexity have decreased by 59.2% and 91.0%, respectively, and the average
detection time per single image has decreased by 5.74 ms. The YOLOv8 cattle posture estimation model has high accuracy and
inference speed, providing a reliable and valuable reference for cattle posture estimation in large-scale animal husbandry.

Key words: posture estimation, skeleton structure, keypoint detection, YOLOv8

CLC Number:

TP391

Chen Bobo, Lei Yuanbin, Zhang Jilei, Zhao Hongyi, Luo Chuang, Zhao Enming. Cattle Posture Estimation Based on YOLO Algorithm[J]. Journal of Dali University, 2024, 9(12): 58-64.

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