在头部运动场景中,基于红外热谱的乳牛的呼吸速率检测
Xinchao Chen1, Baisheng Dai1, Xinjie Wang1
1College of Electrical Engineering and Information, Northeast Agricultural University, Harbin, 150030, China.
Journal of thermal biology
|June 5, 2025
概括
这项研究引入了一种新的方法,用于检测牛的呼吸率 (RR),使用低分辨率的热图像和AI. 这种方法可以准确地监测奶牛的健康状况和热应激,即使是头部运动.
科学领域:
- 动物科学动物科学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 呼吸速率 (RR) 是乳牛健康和热压力的关键指标.
- 红外热图 (IRT) 对鼻孔温度的监测是有效的RR检测.
- 目前的IRT方法面临着由于高分辨率需求和头部运动器件的限制.
研究的目的:
- 开发一种使用低分辨率热图像的自动RR检测方法.
- 为了减轻牛头运动对RR检测准确性的影响.
- 将IRT与深度学习相结合,用于实际的奶牛养殖应用.
主要方法:
- 利用YOLOv8n-Pose网络进行低分辨率的鼻孔关键点识别.
- 使用随机森林算法将像素温度映射到RGB颜色空间.
- 实施了两鼻孔温度曲线融合策略,以减少运动工件.
主要成果:
- 在预测和观察到的RR之间实现了高相关性 (R2=0.92).
- 获得了每分钟3.53次呼吸的根平均平方误差 (RMSE).
- 在246个热视频的数据集上显示了96.3%的平均RR检测准确度.
结论:
- 拟议的方法有效地从低分辨率的热图像中检测到牛RR.
- 集成IRT和深度学习为牛奶农场的RR自动监控提供了一个强大的解决方案.
- 这项技术可以通过改善健康和热应激监测来提高奶牛的福利和管理.
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