基于小型图像集的不同行为类别的个人奶牛识别的多任务对比学习.
J M Hooker1, B B de Medeiros1, C Saha1
1University of Georgia, Athens, GA 30602.
Journal of dairy science
|November 9, 2025
概括
这项研究使用YOLOv7和多任务对比网络 (MTCN) 开发了一种计算机视觉框架,用于在免费系统中自动监控牛行为和个人识别. 该系统准确地跟踪群体行为,并识别单个奶牛,提高奶牛管理.
科学领域:
- 动物科学动物科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自动监测奶牛的行为对于福利和生产率至关重要.
- 现有的方法往往在区分个体动物和不同的行为方面缺乏准确性.
研究的目的:
- 评估YOLOv7算法用于在自由牛群体级别的行为检测.
- 开发和比较一个多任务对比网络 (MTCN) 用于个别牛的识别.
- 创建一个可扩展的两步计算机视觉框架,整合行为检测和识别.
主要方法:
- 利用安装在天花板上的RGB摄像机,在30天内捕捉了21只霍尔斯坦牛的图像.
- 训练了YOLOv7用于群体行为分类和MTCN用于个人牛的识别.
- 通过将预测的行为持续时间 (饮酒,吃饭,休息,站立) 与观察到的值进行比较,验证了框架.
主要成果:
- YOLOv7实现了90.5%的全球准确度,用于群体行为分类.
- 在全球范围内,MTCN实现了83.6%的准确度来识别个别奶牛,其表现优于基线模型.
- 综合框架显示,预测和观察行为持续时间之间存在很强的相关性 (0.78-0.93).
结论:
- 拟议的计算机视觉框架可靠地分类群体行为并识别单个奶牛.
- 这种自动化系统可以持续监测自由制乳制品系统中的行为特征.
- 这项技术有可能改善奶牛群的管理和动物福利.
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