基于伪逆转换和YOLOv5的PCB缺陷检测
Xiaoli Wang1,2, Siti Sarah Maidin2, Malathy Batumalay2
1Artificial Intelligence Department, Shanxi Polytechnic College, Taiyuan, China.
PloS one
|December 12, 2024
概括
本研究介绍了一种改进的YOLOv5方法,用于印刷电路板 (PCB) 缺陷检测,提高复杂集成电路的图像恢复和检测准确性.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 集成电路包装的进步导致复杂的印刷电路板 (PCB) 布局.
- 传统的缺陷检测方法缺乏现代PCB所需的精度.
研究的目的:
- 为PCB开发一种高效准确的缺陷检测方法.
- 解决传统方法在高精度PCB检查中的局限性.
主要方法:
- 为了提高图像清晰度,开发了一个缺陷图像恢复模型.
- 建议采用改进的YOLOv5模型,包括变压器,并优化批量正常化和损失功能.
主要成果:
- 图像恢复模型显示恢复速度增加了37.60%-42.38%.
- 拟议的PCB缺陷检测模型实现了平均召回增加10.90%,平均检测准确度增加12.87%.
结论:
- 开发的方法显著提高了图像处理质量和YOLOv5性能,用于PCB缺陷检测.
- 该方法在现有技术上表现出卓越的有效性,在各种缺陷上具有高精度 (超过98.52%).
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