基于改进的YOLOv5算法,对机械自动食品包装缺陷检测模型的研究
Guanyong Liu1, Shuai Zhang1, Lixin Wang1
1Internship and Training Management Office, Binzhou Polytechnic, Binzhou, Shandong, China.
PloS one
|April 24, 2025
概括
这项研究引入了一种增强的YOLOv5模型,用于自动检测食品包装缺陷,大大提高了比传统方法的准确性和效率. 这种先进的模型擅长识别微妙的缺陷和小目标,提高工业自动化能力.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 工业自动化 工业自动化
背景情况:
- 传统的手动检查食品包装缺陷的方法是低效的,容易出错.
- 现代工业自动化需要高效率和精确的缺陷检测.
- 现有的自动化系统难以识别小目标和微妙的缺陷.
研究的目的:
- 开发基于YOLOv5的增强模型,用于准确有效地检测食品包装缺陷.
- 改善食品包装中小目标和微妙缺陷的识别.
- 提高食品包装检查过程的自动化和精度.
主要方法:
- 集成了一个卷积块注意模块 (CBAM) 来增强特征优先级.
- 采用金字塔和聚合网络进行多级特征融合以捕捉各种缺陷.
- 优化了YOLOv5骨干,采用了精简的YOLOv5s模型和自适应空间特征融合 (ASFF),以改善特征混合.
主要成果:
- 改进的YOLOv5模型实现了卓越的性能,精度 (Ac) =0.96,回忆 (Re) =0.94,F1得分=0.94.
- 它的性能优于原来的YOLOv5 (Ac=0.82,Re=0.85,F1=0.88),YOLOv5+CBAM (Ac=0.88,Re=0.9,F1=0.89),以及YOLOv5+ASFF (Ac=0.94,Re=0.95,F1=0.94) 的性能.
- 组合模型 (CBAM+FPN+PANet+ASFF) 显示了与相关研究工作相比的性能.
结论:
- 提议的增强型YOLOv5模型显著改善了自动化食品包装缺陷检测.
- 集成CBAM,多尺度聚变和ASFF有效地解决了检测小微缺陷的局限性.
- 开发的系统提供了一个强大的解决方案,用于提高食品生产质量控制的自动化和准确性.
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