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  • 1Department of Preventive Veterinary Medicine and Animal Health, School of Veterinary Medicine and Animal Science, Center for Comparative Studies in Sustainability, Health and Welfare, University of São Paulo, Pirassununga, SP, 13635-900, Brazil. tauanamariapaula@gmail.com.

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在母猪中早期检测是具有挑战性的. 这项研究开发了一种使用深度学习的计算机视觉模型,自动跟踪母猪身体的关键点,从而实现客观的的评估和改善动物福利.

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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:

  • 母猪的导致疼痛和福利问题,在早期阶段往往没有被发现.
  • 需要自动化,非侵入性系统来准确和可靠地检测.
  • 目前的方法缺乏客观性和精确性,阻碍了及时干预.

研究的目的:

  • 创建一个全面的图像和视频存储库的母猪与不同的机动分数.
  • 开发计算机视觉模型,用于自动识别和跟踪母猪体关键点.
  • 通过深度学习促进动力学研究,用于客观地检测.

主要方法:

  • 在一个专门的农场设置中收集了不同的母猪的2D视频.
  • 使用两台立体相机进行视频录制.
  • 由13名机动专家使用Zinpro运动分数系统进行注释的视频.
  • 使用SLEAP框架培训和测试深度学习模型.

主要成果:

  • 开发的模型准确地跟踪了6个 (侧面) 和10个 (背部) 骨架关键点.
  • 实现了高性能指标:平均精度 (0.90侧面,0.72背面),低像素距离 (6.83侧面,11.37背面) 和高相似性 (0.94侧面,0.86背面).
  • 证明了客观姿势估计和脚得分的潜力.

结论:

  • 开发的计算模型作为精密畜牧工具用于自动猪姿势分析.
  • 该注释视频库对于动物运动的教学和研究非常有价值.
  • 基于这些发现的自动化系统可以客观地评估母猪的运动分数,提高动物福利.