用于动物识别的人工智能:开发计算机视觉系统,在整个发育阶段可靠地追踪乳牛
A Negreiro1, R E P Ferreira1, T Bresolin2
1Department of Animal and Dairy Sciences, University of Wisconsin, Madison, WI 53706.
Journal of dairy science
|February 12, 2026
概括
这项研究表明,罗神经网络 (SNN) 可以使用不同生命阶段的红外图像准确地识别单个乳牛. 计算机视觉系统为牛的非侵入性追踪提供了一个可行的解决方案.
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:
- 个人动物识别对于乳牛管理,监管合规和粮食安全至关重要.
- 现有的计算机视觉系统 (CVS) 在识别跨生命阶段和开放场景中的动物方面存在局限性.
- 罗神经网络 (SNN) 显示出对非侵入性,纵向动物识别的前景.
研究的目的:
- 评估SNN在识别乳牛在整个生长和生理变化过程中的有效性.
- 调查时间增长对SNN模型性能的影响.
- 在现实世界农场条件下评估SNN的开放集推理能力.
主要方法:
- 收集了来自106只荷尔斯坦公牛的2,397,338张红外和深度图像 (从出生到第一次哺乳期).
- 利用U-Net进行语义细分和图像增强,用于数据预处理.
- 使用一个SNN与ResNet-50骨干,训练有分类交叉和三倍损失.
主要成果:
- 最好的SNN模型在封闭式评估中实现了94.6%的准确性和96.4%的平均精度.
- 在开放式推理中,F1得分在所有生长阶段都超过0.80.
- 红外成像显示出高于深度成像的性能,用于纵向识别.
结论:
- 计算机视觉系统,特别是SNNs,可用于在不同生长阶段和条件下进行非侵入性牛的追踪.
- 红外成像对于长期的个体动物识别比深度成像更有效.
- 建议在商业环境和跨多个群体进行进一步的验证.
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