基于YOLOv4的阿尔巴斯天山羊的检测和定位
Ying Guo1,2, Xihao Wang3, Mingjuan Han3
1School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China.
Animals : an open access journal from MDPI
|October 28, 2023
概括
这项研究引入了一种高效的机器视觉方法来定位山羊,防止牲畜流浪. 开发的算法实现了高精度和速度,有助于可持续的牧场管理.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 动物科学动物科学
背景情况:
- 畜牧业管理,特别是自由放牧的山羊,面临着流浪动物的挑战.
- 有效的局部化系统对于优化草原资源利用和牧场可持续性至关重要.
研究的目的:
- 开发一种高效的基于机器视觉的方法来定位阿尔巴斯天山羊.
- 为了防止山羊流浪,并改善牧场管理实践.
主要方法:
- 提出了一个浅层卷积神经网络 (ShallowSE),使用SENet,GeLU和层规范化来检测山羊.
- 设计了三个完全连接的坐标回归网络模型,用于空间坐标预测.
- 组合目标检测和协调回归算法用于群体定位.
主要成果:
- 拟议的算法实现了95.89%的平均检测精度,检测时间为8.5 ms.
- 定位算法显示平均定位误差为0.94m,定位时间为0.21s.
- 与其他流行的算法相比,该系统在检测准确度和本地化成功率方面表现出卓越的性能.
结论:
- 开发的机器视觉方法提供了快速而准确的山羊定位.
- 这项技术可以为合理化草原资源使用和促进可持续的牧场开发做出重大贡献.
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