基于SCF-YOLO的轻质PCB缺陷检测方法
Yazhou Li1, Yuanyuan Wang1, Jiange Liu2
1College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China.
PloS one
|April 7, 2025
概括
本研究介绍了SCF-YOLO,这是一种用于实时检测印刷电路板 (PCB) 缺陷的轻型模型. 它显著减少了模型大小,并提高了检测速度,使其成为资源有限的工业应用的理想选择.
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:
- 印刷电路板 (PCB) 的实时缺陷检测面临着大型模型尺寸和低速度的挑战.
- 资源限制阻碍了在工业环境中部署有效的缺陷检测算法.
研究的目的:
- 为PCB开发一种轻量级和高效的缺陷检测模型.
- 在复杂的PCB检查场景中解决大型模型尺寸和缓慢推断速度的局限性.
主要方法:
- 建议使用MobileNet作为轻量级特征提取网络的SCF-YOLO模型.
- 在部引入了一个可学习的加权特征融合模块,用于多尺度的特征增强.
- 开发了一种新的SCF (合成C2f) 模块,以改进高级语义特征捕获.
- 采用了CIoU和GIoU损失函数的组合功能,用于精确的缺陷定位.
主要成果:
- 与YOLOv8.8相比,SCF-YOLO显示参数减少了25%.
- 与YOLOv8.8相比,检测速度提高了多达60%.
- 该模型有效地增强了特征表达,并专注于关键缺陷特征.
结论:
- SCF-YOLO为PCB缺陷检测提供了一个快速,准确和高效的解决方案.
- 轻量化设计使其适合在资源有限的工业环境中部署.
- 这一进步有助于提高PCB制造中的质量控制.
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