基于YOLOv8n和深度SORT的增强动态猪计数研究
Peng Shen1, Keyu Mei1, Haori Xue1
1North China Institute of Aerospace Engineering, School of Aeronautics and Astronautics, Langfang 065000, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
本研究介绍了一种改进的YOLOv8n-EGV+Deep SORT-P算法,用于准确的动态猪计数. 改进后的模型显著提高了计数精度和跟踪稳定性,解决了手动方法的局限性.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉 计算机视觉
- 动物科学动物科学
背景情况:
- 手动计数猪是劳动密集型和不准确的.
- 现有的自动化方法在检测和跟踪诸如拥挤和封闭等挑战方面扎.
研究的目的:
- 为准确和稳定的动态猪计数开发一个增强的算法.
- 改进真实世界养殖环境中的猪目标识别和跟踪精度.
主要方法:
- 将ELA注意力,GSConv和VoVGSCSP模块集成到YOLOv8n中,以改进检测.
- 通过DenseNet和CIoU增强深度SORT,以实现强大的跟踪.
- 使用来自农场通道的猪视频进行验证.
主要成果:
- 改进的YOLOv8n-EGV+Deep SORT-P算法实现了92.1%的计数精度,提高了17.5%.
- 增强的深度SORT-P跟踪显示MOTA (89.2%) 和MOTP (90.4%) 的改善,IDSW.减少.
- 该算法在实际环境中证明了稳定的动态猪计数.
结论:
- 拟议的算法比现有的动态猪计数方法有了显著的进步.
- 这项技术为精准畜牧养殖和管理提供了有价值的数据.
- 改进后的系统解决了自动化动物计数的关键问题.
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