基于EDF-YOLOv10的印刷电路板的缺陷检测方法
Zhijuan Shen1, Yonger Yao1, Lin Liu1
1School of Mechanical, Electronic & Information Engineering, Putian University, Putian, Fujian, China.
本研究介绍了一种增强的YOLOv10算法 (EDF-YOLOv10) 用于印刷电路板 (PCB) 缺陷检测,提高了小物体识别和融合速度. 还开发了一个实时检测系统,实现了显著的性能提升.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 电气工程 电气工程
背景情况:
- 印刷电路板 (PCB) 缺陷检测面临着小物体表示和缓慢模型融合的挑战.
- 现有的方法难以应对复杂的工业环境和强大的特征提取.
研究的目的:
- 提出一个改进的YOLOv10算法 (EDF-YOLOv10),用于增强PCB缺陷检测.
- 开发一个实时,协同优化的硬件和软件检测系统.
- 改进小缺陷的特征表示,加快模型的融合.
主要方法:
- 集成的高效通道注意力 (ECA) 用于关键通道特征提取.
- 利用动态蛇卷积 (DSConv) 来改进小型目标的几何结构识别.
- 实施了Focaler-CIoU损失以增强硬样本学习和趋同.
- 在PKU-Market-PCB数据集中应用了广泛的数据增强,以提高稳定性.
- 开发了一个实时检测系统,配有工业摄像头,可控制的照明和PyQt5 GUI.
主要成果:
- 欧洲开发基金YOLOv10模型实现了90.6%的mAP@0.50和48.4%的mAP@0.50:0.95.
- 与基线模型相比,分别显示了3.0%和1.6%的改进.
- 实时检测系统成功地集成了硬件和软件,用于实际应用.
结论:
- 拟议的EDF-YOLOv10算法有效地解决了PCB缺陷检测的局限性,特别是对于小物体.
- 开发的实时系统为工业应用提供了强大的解决方案.
- 这项工作为复杂的工业缺陷检测场景提供了宝贵的方法参考.
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