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一种长期视频追踪方法,用于集体养的猪.

Qiumei Yang1,2, Xiangyang Hui1,2, Yigui Huang1,2

  • 1College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.

Animals : an open access journal from MDPI
|May 25, 2024
PubMed
概括

这项研究引入了对猪的改进追踪算法,提高了农场环境中的准确性和稳定性. 这种新方法可以持续24小时对猪进行监测,从而支持更好的农场管理.

关键词:
深度学习是一种深度学习.多对象跟踪多对象跟踪对象检测检测对象检测对象检测猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪猪

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科学领域:

  • 计算机视觉 计算机视觉
  • 动物科学动物科学
  • 农业技术 农业技术

背景情况:

  • 准确的猪追踪对于养殖场管理至关重要,但由于堵塞和动作模糊而具有挑战性.
  • 现有的多猪追踪方法在现实世界养殖条件下的长期,持续监测方面存在困难.

研究的目的:

  • 开发一种强大的,长期的视频追踪方法,用于集体养的猪.
  • 提高生产环境中猪追踪的效率和准确性.
  • 为非接触式,自动猪监测提供技术支持.

主要方法:

  • 提出了一个改进的StrongSORT算法,用于增强猪跟踪.
  • 开发了一种轻量级的猪检测网络 (YOLO v7-tiny_Pig) 来实现更快的检测.
  • 优化了轨迹管理,以减少身份交换机和提高跟踪稳定性.
  • 构建了一个24小时的猪追踪视频数据集.

主要成果:

  • YOLO v7-tiny_Pig将参数减少了36.7%,并实现了435 FPS的检测速度.
  • 追踪算法获得了很高的分数:83.16%的HOTA,97.6%的MOTP和91.42%的IDF1.
  • 与原来的StrongSORT相比,HOTA提高了6.19%,IDF1提高了10.89%,IDSW减少了69%.

结论:

  • 拟议的方法在实际养殖场景中显著提高了猪追踪性能.
  • 该算法可连续,稳定地跟踪猪长达24小时.
  • 这种非接触式监测方法为现代养猪业提供了宝贵的技术支持.