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基于改进的YOLOV5的牧羊行为识别.

Tianci Hu1,2, Ruirui Yan3, Chengxiang Jiang1,2

  • 1College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.

Sensors (Basel, Switzerland)
|July 11, 2023
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概括

这项研究引入了一种增强的You Only Look Once Version 5 (YOLOV5) 算法,用于识别牧场上的绵羊行为. 改进的模型达到90%以上的准确性,有助于精确的畜牧管理.

关键词:
行为识别,行为识别.放牧的羊在放牧的时间.改进了YOLOV5的功能.牧场 牧场 牧场 牧场 牧场

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科学领域:

  • 计算机视觉 计算机视觉
  • 动物行为 动物行为
  • 精确的畜牧管理 精确的畜牧管理

背景情况:

  • 绵羊行为监测对于生理健康评估至关重要,但由于条件变化,在放牧环境中具有挑战性.
  • 在自由放牧环境中准确识别羊的行为需要强大的算法来解决照明和环境变化.

研究的目的:

  • 开发和评估一个改进的羊行为识别算法,使用你只看一次版本5 (YOLOV5) 模型.
  • 调查不同拍摄方法和环境条件对模型性能和概括性的影响.
  • 提出实践应用中实时识别羊行为的系统设计.

主要方法:

  • 使用两种不同的射击方法构建绵羊行为数据集.
  • 应用和增强YOLOV5模型,包括集成注意力机制模块.
  • 交叉验证技术用于评估模型在不同环境条件下的概括能力.
  • 设计基于云的结构,结合实时消息协议 (RTMP) 进行实时视频流处理.

主要成果:

  • 在开发的数据集上,YOLOV5模型在三个行为分类中实现了超过90%的平均准确性.
  • 交叉验证表明,使用手持摄像头数据训练的模型表现出优越的概括能力.
  • 带有注意力机制的增强YOLOV5模型实现了91.8%的平均平均精度 (mAP@0.5),提高了1.7%.

结论:

  • 该研究成功开发了一种改进的YOLOV5算法,用于在牧场环境中准确识别绵羊行为.
  • 拟议的模型有效地检测每日绵羊的行为,为精确的畜牧管理和现代畜牧业提供了重大潜力.
  • 实时识别系统的设计为农业环境中的实际实施提供了一条途径.