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基于YOLOv8-ByteTrack的玉米核批量计数系统
Ran Li1, Qiming Liu1, Miao Wang1
1School of Engineering, Anhui Agricultural University, Hefei 230036, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究介绍了使用深度学习的实时玉米核计数系统. 新的卷积神经网络 (CNN) 方法实现了99%的准确性,克服了自动化食品加工方面的挑战.
科学领域:
- 食品工程 食品工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 玉米核数量对于作物评估和产量预测至关重要.
- 传统的计数方法在高速运动,遮蔽和目标ID切换方面遇到了困难.
研究的目的:
- 为玉米开发一个实时的核子下降计数系统.
- 为了提高自动化食品加工环境的准确性和稳定性.
主要方法:
- 实施了一个基于卷积神经网络 (CNN) 的系统.
- 集成YOLOv8对象检测与ByteTrack多对象跟踪.
- 使用高速摄像头进行动态视频流捕获.
主要成果:
- 实现了高达99%的跟踪和计数准确度.
- 有效地克服了来自高速运动和物体封闭的计数错误.
- 在复杂的条件下证明了增强的系统稳定性.
结论:
- 开发的系统提供智能和精确的内核计数.
- 为自动化质量监测和产量估计提供可靠的技术支持.
- 显示了食品加工生产线的重要应用价值.
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