在复杂的开放牧场环境中优化黑牛跟踪,使用YOLOv8嵌入式多摄像头系统
Su Myat Noe1, Thi Thi Zin2, Ikuo Kobayashi3
1Interdisplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki, 889-2192, Japan.
Scientific reports
|February 25, 2025
概括
本研究介绍了一种定制的多摄像头多牛追踪 (MCMCT) 系统,用于精确监测黑牛. 该系统达到95.61%的准确性,改善了精准农业中的牲畜管理.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 动物科学动物科学
背景情况:
- 日常活动监测对于黑牛的福利和农场管理至关重要.
- 人工智能和计算机视觉的进步为畜牧业监测提供了新的可能性.
- 传统的单摄像头系统在复杂的开放牧场环境中难以准确跟踪.
研究的目的:
- 开发一种新的定制多摄像头多牛追踪 (MCMCT) 系统,用于精确监测黑牛.
- 克服单摄像头设置在开放牧场环境中检测和跟踪牛的局限性.
- 提高精准农业中的牲畜行为分析,健康评估和期事件预测.
主要方法:
- 该MCMCT系统使用了跟踪通过检测模型,并以YOLO v8细分模型作为骨干.
- 采用多摄像头 (四个摄像头) 的方法来改善复杂环境中的覆盖和准确性.
- 与细分任何模型 (SAM) 集成可自动提取牛口罩区域,减少手动标签.
主要成果:
- 在使用4个摄像头的10个案例中,YOLOv8-MCMCT系统实现了平均95.61%的多对象跟踪精度 (MOTA).
- 该系统以每秒30的速度处理数据,展示了高效的实时性能.
- 对比分析显示,MCMCT的性能优于Bot-sort,Byte-track和OC-sort等最先进的跟踪方法.
结论:
- 在精密畜牧业中,MCMCT系统提供了一个强大的非接触式解决方案,用于自动监测牲畜.
- 该系统在各种农场环境中表现出高精度和适应性,无需进行广泛的再培训.
- 这项研究有助于改善畜牧业监测,农业效率和农场管理实践.
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