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

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaTrends in Artificial Intelligence and Computer Engineering - Proceedings of ICAETT 2021
EditoresMiguel Botto-Tobar, Omar S. Gómez, Raul Rosero Miranda, Angela Díaz Cadena, Sergio Montes León, Washington Luna-Encalada
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas199-208
Número de páginas10
ISBN (versión impresa)9783030961466
DOI
EstadoPublicada - 2022
Evento3rd International Conference on Advances in Emerging Trends and Technologies, ICAETT 2021 - Riobamba, Ecuador
Duración: 10 nov. 202112 nov. 2021

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen407 LNNS
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

Conferencia3rd International Conference on Advances in Emerging Trends and Technologies, ICAETT 2021
País/TerritorioEcuador
CiudadRiobamba
Período10/11/2112/11/21

Nota bibliográfica

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

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