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基于计算机视觉的子的姿势检测方法

Cheng Fang1, Xiaolin Zhuang1, Haikun Zheng2

  • 1College of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, China.

Animals : an open access journal from MDPI
|November 9, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种计算机视觉方法,可以自动检测子的姿势,从而减少家禽养殖中人工劳动. 该系统使用图像处理和深度摄像头数据准确识别站立和躺着的.

关键词:
在子里养的肉计算机视觉 计算机视觉图像的深度图像的深度图像.姿势检测 姿势检测智能农业 智能农业

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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:

  • 手动监控子是劳动密集型和耗时的.
  • 需要自动化系统来提高家禽养殖的效率和福利.
  • 姿势分析为动物健康和行为提供了洞察力.

研究的目的:

  • 开发一种基于计算机视觉的自动化方法来检测子的姿势.
  • 为了减少监测在养殖场的状态所涉及的手工劳动.
  • 为分析的行为和福利提供一种非侵入性工具.

主要方法:

  • 图像校正技术用于标准化图像定向.
  • 差异和加快稳固特征 (SURF) 方法被用于定位养槽和定义关键区域.
  • 使用深度摄像头提取3D信息,并在关键区域内对进行细分.
  • 限制条件被应用到选和分类的姿势 (站立/躺着).

主要成果:

  • 该算法实现了高精度和回忆率的姿势检测在白 (97.80%的精度, 80.18%的回忆) 和 (79.52%的精度, 81.07%的回忆).
  • 该系统展示了高效的处理,在i5-8500 CPU上以每秒10的速度运行.
  • 该方法在从复杂的子环境中提取并分析它们的姿势时证明有效.

结论:

  • 拟议的计算机视觉方法提供了一种准确和有效的非侵入性方法来监测子的姿势.
  • 这项技术有可能通过自动化状态检查来显著改善家禽管理.
  • 这些发现有助于推进精密家禽养殖和动物福利研究.