机电工程技术2024，Vol.53Issue(2)：225-229,5.DOI:10.3969/j.issn.1009-9492.2024.02.049
基于改进YOLOv5的PCB缺陷检测方法研究
Research on PCB Defect Detection Method Based on Improved YOLOv5
黄熙 1朱兆优 1叶海鹏 1刘达1
作者信息
- 1. 东华理工大学机械与电子工程学院,南昌 330013
- 折叠
摘要
Abstract
With the development of industrial manufacturing,printed circuit board(PCB)is becoming more and more important in the manufacture of electronic products.In the process of PCB production,there are many kinds of bad defects,so an efficient PCB defect detection method is urgently needed.A PCB defect detection method based on improved YOLOv5 is proposed to solve the problem of low accuracy of small target defect detection in PCB image detection in traditional YOLOv5 target detection algorithm.Firstly,in order to solve the problem of missing detection of small target defects,an efficient channel attention mechanism(SE)module is added to the feature extraction network of YOLOv5 to improve the feature extraction ability of small target defects,thus improving the detection accuracy of small target defects.Secondly,in order to optimize and improve the original YOLOv5 algorithm,a weighted loss function is used to replace the original loss function,so as to fully learn various features of the image.The test is carried out on the PCB defect data set published by Peking University Robotics Laboratory.The experimental results show that the improved model improves the small target defect detection effect,and its mAP value is 94.54%,which is 2.1%higher than the original algorithm model.It can accurately complete the defect detection task of printed circuit boards in industrial production.关键词
PCB/缺陷检测/YOLOv5/注意力机制/损失函数Key words
PCB/defect detection/YOLOv5/attention mechanism/loss function分类
信息技术与安全科学引用本文复制引用
黄熙,朱兆优,叶海鹏,刘达..基于改进YOLOv5的PCB缺陷检测方法研究[J].机电工程技术,2024,53(2):225-229,5.
