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在3D视觉中使用后面表面点云进行个人猪识别.

Hong Zhou1, Qingda Li1, Qiuju Xie2,3

  • 1College of Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.

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PubMed
概括
此摘要是机器生成的。

对于精准畜牧业来说,个体猪的识别是至关重要的. 一种新方法使用3D后面点云和改进的PointNet++LGG算法,实现95.26%的准确性,以有效管理农场.

关键词:
3D传感器 3D传感器在 PointNet++++ 中使用.深度学习是一种深度学习.猪个体识别 猪个体识别点云点云是指点云.

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科学领域:

  • 农业工程 农业工程
  • 计算机视觉 计算机视觉
  • 动物科学 动物科学

背景情况:

  • 对于精准畜牧业 (PLF) 应用,如养和健康监测,个人猪识别是必不可少的.
  • 传统的猪脸部识别方法面临挑战,原因是数据收集和环境干扰的困难.
  • 现有的方法在区分具有相似身体特征的猪方面缺乏稳定性.

研究的目的:

  • 开发一个强大的个人猪识别系统,使用3D后面点云.
  • 克服猪在具有挑战性的农场环境中面部识别的局限性.
  • 通过精确的动物识别,提高精确畜牧业的能力.

主要方法:

  • 利用基于PointNet++的模型从复杂的背景对猪背点云进行细分.
  • 开发了一种改进的PointNet++LGG算法,具有自适应采样和更深入的网络结构,用于特征提取.
  • 收集了来自十头猪的10,574张3D点云图像的数据集,用于模型培训和验证.

主要成果:

  • 改进的PointNet++LGG算法实现了个体猪识别准确率95.26%.
  • 这种精度超过了现有的模型,比PointNet提高了2.18%,比PointNet++SSG提高了16.76%,比MSG提高了17.19%.
  • 三维后面点云方法在识别猪个体方面表现出高效.

结论:

  • 对猪背表面的3D点云分析为个人识别提供了有效的替代方案.
  • 这种方法与身体状况评估和行为识别的整合相兼容.
  • 开发的方法支持精密畜牧技术的进步.