Multiple Detection Model Fusion Framework for Printed Circuit Board Defect Detection

Xing Wu*, Qingfeng Zhang, Jianjia Wang, Junfeng Yao, Yike Guo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The printed circuit board (PCB) is an indispensable component of electronic products, which determines the quality of these products. With the development and advancement of manufacturing technology, the layout and structure of PCB are getting complicated. However, there are few effective and accurate PCB defect detection methods. There are high requirements for the accuracy of PCB defect detection in the actual production environment, so we propose two PCB defect detection frameworks with multiple model fusion including the defect detection by multi-model voting method (DDMV) and the defect detection by multi-model learning method (DDML). With the purpose of reducing wrong and missing detection, the DDMV and DDML integrate multiple defect detection networks with different fusion strategies. The effectiveness and accuracy of the proposed framework are verified with extensive experiments on two open-source PCB datasets. The experimental results demonstrate that the proposed DDMV and DDML are better than any other individual state-of-the-art PCB defect detection model in F1-score, and the area under curve value of DDML is also higher than that of any other individual detection model. Furthermore, compared with DDMV, the DDML with an automatic machine learning method achieves the best performance in PCB defect detection, and the F1-score on the two datasets can reach 99.7% and 95.6% respectively.

Original languageEnglish
Pages (from-to)717-727
Number of pages11
JournalJournal of Shanghai Jiaotong University (Science)
Volume28
Issue number6
DOIs
Publication statusPublished - Dec 2023
Externally publishedYes

Keywords

  • A
  • TP 399
  • defect detection
  • model fusion
  • object detection model
  • printed circuit board (PCB)

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