System for Troubleshooting Welded Printed Circuit Boards with Through Hole Technology Using Convolutional Neural Networks and Classic Computer Vision Techniques

Alberto Santiago Ramirez-Farfan, Miguel Angel Quiroz-Martinez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Manual inspection in printed circuit board manufacturing is highly susceptible to failure through human error. This opens the way for automated visual inspection. Several methods exist for detection based on images captured by a camera. The objective of this work is to develop a computer vision system using convolutional neural networks and classical computer vision techniques for locating soldering faults on printed circuit boards with through-hole technology. For this purpose, the OpenCV library on Python is used to detect the region of interest within the image prior to the analysis and classification of the convolutional neural network ResNET50. Two types of faults were presented as lack of solder and solder bridge. The results obtained in the experimental classification tests have an accuracy margin higher than 90%. This makes a viable use of automated visual inspection in the testing and inspection processes of errors in the soldering of printed circuit boards. The dataset is available at: https://github.com/asrf001/DatasetPCB.git.

Original languageEnglish
Title of host publicationTrends in Artificial Intelligence and Computer Engineering - Proceedings of ICAETT 2021
EditorsMiguel Botto-Tobar, Omar S. Gómez, Raul Rosero Miranda, Angela Díaz Cadena, Sergio Montes León, Washington Luna-Encalada
PublisherSpringer Science and Business Media Deutschland GmbH
Pages199-208
Number of pages10
ISBN (Print)9783030961466
DOIs
StatePublished - 2022
Event3rd International Conference on Advances in Emerging Trends and Technologies, ICAETT 2021 - Riobamba, Ecuador
Duration: 10 Nov 202112 Nov 2021

Publication series

NameLecture Notes in Networks and Systems
Volume407 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference3rd International Conference on Advances in Emerging Trends and Technologies, ICAETT 2021
Country/TerritoryEcuador
CityRiobamba
Period10/11/2112/11/21

Bibliographical note

Funding Information:
This work has been supported by the GIIAR research group and the Universidad Polit?cnica Salesiana.

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Automated visual inspection
  • Classical computer vision
  • Convolutional neural networks
  • OpenCV
  • ResNET50

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