乳牛通过实时面部识别进行生物识别
N Bergman1, Y Yitzhaky2, I Halachmi3
1School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 1 Ben Gurion Avenue, P.O.B. 653, Be'er Sheva 8410501, Israel; Precision Livestock Farming (PLF) Laboratory, Institute of Agricultural Engineering, Agricultural Research Organization (A.R.O.) - The Volcani Center, 68 Hamaccabim Road, P.O.B 15159, Rishon Lezion 7505101, Israel.
Animal : an international journal of animal bioscience
|February 20, 2024
概括
这项研究引入了一种新的深度学习系统,用于使用面部识别识别单个奶牛. 强大的系统在实时实现高精度,为自动化农场管理提供了有价值的工具.
科学领域:
- * * 农业技术 农业技术
- * 计算机视觉 计算机视觉
- * 动物科学 动物科学
背景情况:
- * 传统的奶牛识别方法在现实农场环境中面临动物移动和系统稳定性方面的挑战.
- *生物识别在人类中取得了成功,为乳牛的识别提供了潜力.
- * 需要自动化系统来提高奶牛场管理的效率和准确性.
研究的目的:
- * 开发和评估基于深度学习的系统,用于准确的,实时的,多头牛面部检测和分类.
- * 应对不受控制的动物移动的挑战,并确保农场环境中的系统稳定性.
- * 创建一个有价值的工具,用于牛奶农场的自动生物识别.
主要方法:
- * 实施和调整了最先进的深度学习模型 (YOLOv5用于检测,视觉转换器用于分类).
- * 设计并安装了一个基于视频的系统,放置在一个奶牛场的养区上方.
- * 获取并注释了面部检测和分类的两个数据集,涉及77头奶牛.
主要成果:
- *使用YOLOv5 (IoU 0.5) 实现了97.8%的面部检测平均精度.
- * 通过视觉转换器模型和专门的损失功能实现了96.3%的面部分类准确度.
- * 该系统实时处理多个牛脸 (最多10个) 的视频 (每<20毫秒),支持每秒最多50.
结论:
- * 拟议的深度学习方法可实现高效的实时面部检测和多个牛面部的识别.
- *高精度和实时处理能力使该系统适合在奶牛场的实际应用.
- * 这种自动化生物识别牛识别系统为改善乳牛群管理提供了显著的潜力.
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