基于YOLOv8的非烟草相关材料识别方法的研究
Chunjie Zhang1,2, Lijun Yun3,4, Mingjie Wu1,2
1Yunnan Normal University, Kunming, 650000, China.
Scientific reports
|October 13, 2025
概括
本研究介绍了NTRM-YOLO,这是一个增强的深度学习模型,用于检测烟草加工中的非烟草相关材料. 该模型以更少的参数实现了95.6%的检测准确度,改善了质量控制.
科学领域:
- 农业工程 农业工程
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 提高烟叶纯度对于工业质量控制至关重要.
- 准确检测非烟草相关材料对于原材料加工至关重要.
研究的目的:
- 开发一种增强的深度学习模型,用于准确检测非烟草相关材料.
- 提高检测模型的效率并减少检测模型的参数数量.
主要方法:
- 开发了一种改进的YOLOv8模型,称为NTRM-YOLO.
- 集成的注意力机制,GhostConv和Dyhead模块.
- 使用矢量角度优化损失函数,并使用工业相机数据集.
主要成果:
- NTRM-YOLO的检测性能达到了95.6%,比基线提高了2%.
- 模型的参数数量减少了10%至10.0 MB.
- 在检测非烟草相关材料方面表现出显著的有效性.
结论:
- NTRM-YOLO提供了一种强大的解决方案,用于检测烟草加工中的杂质.
- 该模型为开发先进的工业杂质清除设备提供了基础.
- 增强的深度学习模型可以显著改善烟草质量控制.
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