YOLOv10n-CF-Lite:一种基于自动化注释和转移学习的方法,用于识别胡羊的个体面部
Yameng Qiao1, Wenzheng Liu1, Fanzhen Wang1
1College of Computer and Information Engineering, Tianjin Agricultural University, Tianjin 300392, China.
Animals : an open access journal from MDPI
|September 13, 2025
概括
这项研究引入了一种高效的羊面部识别系统,使用自动注释和转移学习. 开发的算法显著减少了注释时间,并提高了Hu羊的识别准确性,从而实现了更好的牲畜管理.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 对于精确的畜牧管理来说,对胡羊的个别识别至关重要.
- 传统的机器视觉方法用于识别羊是耗时的,需要对新人进行广泛的再培训.
研究的目的:
- 开发一个高效的羊面部识别算法,集成自动注释和转移学习.
- 为了应对手工注释方面的挑战,时间成本和适应新羊个体的适应性.
主要方法:
- 创建了一个由82只胡羊的6055张图像组成的数据集,采用多视图视频收集和数据增强.
- 设计了一种羊脸检测和自动注释算法,将注释时间缩短到0.014分钟/图像.
- 采用YOLOv10n-CF-Lite模型,并应用转移学习来适应该模型的新羊个体.
主要成果:
- 使用YOLOv10n-CF-Lite模型,获得了92.3%的识别精度和96.2%mAP@0.5的Hu羊面.
- 转移学习在目标领域 (新羊) 的识别精度提高到94.9%,mAP@0.5提高到97%.
- 培训时间从43个缩短到14个,大大提高了模型融合速度.
结论:
- 拟议的算法提高了Hu羊面部识别的注释效率,识别精度和融合速度.
- 自动注释和转移学习允许快速适应新的绵羊个体,支持智能畜牧管理.
- 这为精密畜牧业提供了高效可靠的技术解决方案.
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