改进的YOLOv5方法用于准确识别放牧羊的活动:活跃,不活跃,反行为
Xiao Dong1,2, Zirui Zhang1,2, Juan Liao1
1College of Engineering, Anhui Agricultural University, Hefei, China.
Journal of animal science
|April 22, 2025
概括
这项研究引入了一种改进的YOLOv5模型,用于准确识别绵羊行为,特别是反,增强牲畜健康监测. 该模型显著提高了反动物检测的准确性,为了解绵羊需求提供了坚实的基础.
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:
- 准确的绵羊行为监测对于健康和疾病预防至关重要.
- 检测反行为,这是一个关键的消化健康指标,与传统方法具有挑战性.
- 现有的计算机视觉模型往往忽视反行为作为一个独特的特征.
研究的目的:
- 开发一种高效的计算机视觉模型,用于识别羊群中活跃,不活跃和反的行为.
- 为了提高羊行为识别的准确性和效率,特别是在反动物行为方面.
主要方法:
- 开发了一种改进的You Only Look Once-v5 (YOLOv5) 模型,结合了一个完全卷积神经网络和空间金字塔聚合跨阶段部分连接模块.
- 一个新的道连锁优化算法 (CiConcat) 和选择性内核注意力机制被整合在一起.
- 引入了一种检测头匹配策略,以改善远程行为检测.
主要成果:
- 改进后的模型在户外羊群图像上获得了94.1%的平均精度,超过了最先进的方法.
- 动物行为检测精度达到80.2%的平均精度,比标准YOLOv5.5有35.44%的改进.
- 该模型展示了对绵羊行为频率的持续监测能力.
结论:
- 拟议的模型在自动识别绵羊行为方面取得了重大进展,特别是在反动物行为方面.
- 这项技术为分析羊的行为模式和了解它们的生理和行为需求提供了坚实的基础.
- 这些发现支持通过精确的健康和福利监测来改善牲畜管理.
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