对日常牛行为轻量级识别模型的研究 实现实时监测
Jianping Yao1, Yong'an Zhang1, Mei'an Li1
1College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Veterinary sciences
|December 31, 2025
概括
这项研究引入了一种高效的AI模型,用于识别牛的行为,如站立,躺下和吃饭. 轻量级系统可实现实时监控,改善动物福利和早期发现牲畜疾病.
科学领域:
- 动物科学动物科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 精确监测牛的行为对于检测疾病和评估福利至关重要.
- 站立,躺着和饮食时间的变化标志着诸如腿和代谢障碍等健康问题.
研究的目的:
- 开发一种低成本,非侵入性,实时的牛行为识别系统.
- 创建一个适合资源有限的边缘设备的轻量级AI模型.
主要方法:
- 使用了改进的YOLOv11n架构,结合了GELAN用于多规模的功能集成和MCA用于增强的功能提取.
- 双向特征金字塔网络 (BiFPN) 用于高效的跨度融合,而深度可分离卷积 (DWConv) 减少了计算负载.
- 该模型使用基于层适应的幅度修剪 (LAMP) 来进行压缩.
主要成果:
- 该模型实现了高识别精度:91.2% (站着),91.0% (躺着) 和93.9% (吃饭) mAP@0.5.5.
- 在压缩后,该模型具有106万个参数,6.3 GFLOPS,和2.4 MB的重量大小,保留了90.7%的mAP@0.5.5.
- 该系统证明适合于边缘部署,提供实用的连续牛监测.
结论:
- 开发的轻量级AI模型为实时牛行为识别提供了有效的解决方案.
- 这项技术支持精密畜牧业,使得早期的健康干预和提高动物福利.
- 该系统的效率使其非常适合在边缘设备上部署,用于在畜牧管理中进行持续监控.
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