基于改进的YOLOv11的电子产品表面缺陷检测方法
Jianming Meng1, Longjian Guo1, Wei Hao1
1Department of Electronic and Communication Engineering, Shandong College of Electronic Technology, Jinan, China.
PloS one
|October 28, 2025
概括
本研究引入了一种改进的YOLOv11模型,用于在电子产品中增强表面缺陷检测. 新方法显著提高了精度和召回率,为制造质量控制提供了更可靠的自动化解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 制造业 制造技术 制造技术
背景情况:
- 电子产品的传统手动检查方法受到操作员的变化性限制,并与对效率和精度的日益增长的要求作斗争.
- 深度学习,特别是对象检测,在制造业自动化质量控制方面显示出前景.
- 现有的YOLO模型在复杂的背景下检测小缺陷方面面临挑战.
研究的目的:
- 开发一个改进的基于YOLOv11的深度学习模型,用于准确检测电子产品的表面缺陷.
- 解决当前YOLO模型在识别小缺陷和复杂背景方面的局限性.
- 为了提高自动缺陷检测系统的精度,回忆率和检测速度.
主要方法:
- 提出了改进的YOLOv11架构,其中包括MD-C2F模块,DualConv模块和Inner_MPDIoU损失函数.
- 该模型经过训练和评估,用于电子产品的表面缺陷检测.
- 性能与之前的YOLO版本 (YOLOv7,YOLOv8,YOLOv9) 以及PKU-Market-PCB数据集进行了比较.
主要成果:
- 改进的YOLOv11模型实现了93.1%的精度 (高于90.9%) 和84.6%的召回率 (高于77.0%).
- mAP50增长了4.6%至88.6%,在检测电阻,LED灯和电容器等各种缺陷方面超过了其他YOLO版本.
- 在PKU-Market-PCB数据集上的泛化测试显示了准确度 (94.6%),回忆 (91.2%) 和mAP50 (95.4%) 的提高.
结论:
- 拟议的YOLOv11模型有效地克服了在复杂的背景和不同尺度中检测小缺陷的挑战.
- 检测准确度,回忆和概括能力显著提高.
- 改进的YOLOv11为电子产品制造中的缺陷检测提供了强大的自动化解决方案.
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