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一个改进的猪计数算法基于YOLOv5和DeepSORT模型.

Yigui Huang1,2, Deqin Xiao1,2, Junbin Liu1,2

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

Sensors (Basel, Switzerland)
|July 29, 2023
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概括
此摘要是机器生成的。

本研究介绍了一种使用YOLOv5x和DeepSORT的改进猪计数算法 (MPC-YD),用于在农业中高效准确的自动猪计数. 新方法提高了检测和跟踪,在现实场景中显示高精度.

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

背景情况:

  • 手动计数猪是低效的,昂贵的,并且阻碍了统计分析.
  • 现有的猪视频跟踪在特征检测,由于快速移动而导致的跟踪损失和计数偏差方面面临挑战.

研究的目的:

  • 开发一个改进的猪计数算法 (MPC-YD),解决当前自动化系统的局限性.
  • 提高养殖和屠宰场环境中猪计数的准确性和效率.

主要方法:

  • 一个增强的YOLOv5x模型,包含两个大小的SPP网络和SoftPool操作,以改进猪部分检测.
  • 集成猪重新识别网络和空间状态校正,以准确跟踪猪.
  • 在DeepSORT中开发了一个数判断方法,用于精确计数.

主要成果:

  • 在猪物体检测中,MPC-YD算法实现了99.24%的平均精度.
  • 在多目标猪追踪中证明了85.32%的准确性.
  • 在屠宰场通道中基于视频的猪计数实现了98.14%的相关系数 (R2),在繁殖环境中表现稳定.

结论:

  • 与现有方法相比,MPC-YD算法显著提高了猪计数的准确性和效率.
  • 该算法显示出强大的适用性和潜力,用于现实世界养猪和管理.
  • 这种先进的系统为自动化牲畜监测提供了可行的解决方案.