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Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning
As the size of components mounted on printed circuit boards (PCBs) decreases, defect detection becomes more important. The first step in an inspection involves recognizing and inspecting characters printed on parts attached to the PCB. In addition, since industrial fields that produce PCBs can chang...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122424/ https://www.ncbi.nlm.nih.gov/pubmed/33919360 http://dx.doi.org/10.3390/s21092921 |
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author | Gang, Sumyung Fabrice, Ndayishimiye Chung, Daewon Lee, Joonjae |
author_facet | Gang, Sumyung Fabrice, Ndayishimiye Chung, Daewon Lee, Joonjae |
author_sort | Gang, Sumyung |
collection | PubMed |
description | As the size of components mounted on printed circuit boards (PCBs) decreases, defect detection becomes more important. The first step in an inspection involves recognizing and inspecting characters printed on parts attached to the PCB. In addition, since industrial fields that produce PCBs can change very rapidly, the style of the collected data may vary between collection sites and collection periods. Therefore, flexible learning data that can respond to all fields and time periods are needed. In this paper, large amounts of character data on PCB components were obtained and analyzed in depth. In addition, we proposed a method of recognizing characters by constructing a dataset that was robust with various fonts and environmental changes using a large amount of data. Moreover, a coreset capable of evaluating an effective deep learning model and a base set using n-pick sampling capable of responding to a continuously increasing dataset were proposed. Existing original data and the EfficientNet B0 model showed an accuracy of 97.741%. However, the accuracy of our proposed model was increased to 98.274% for the coreset of 8000 images per class. In particular, the accuracy was 98.921% for the base set with only 1900 images per class. |
format | Online Article Text |
id | pubmed-8122424 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81224242021-05-16 Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning Gang, Sumyung Fabrice, Ndayishimiye Chung, Daewon Lee, Joonjae Sensors (Basel) Article As the size of components mounted on printed circuit boards (PCBs) decreases, defect detection becomes more important. The first step in an inspection involves recognizing and inspecting characters printed on parts attached to the PCB. In addition, since industrial fields that produce PCBs can change very rapidly, the style of the collected data may vary between collection sites and collection periods. Therefore, flexible learning data that can respond to all fields and time periods are needed. In this paper, large amounts of character data on PCB components were obtained and analyzed in depth. In addition, we proposed a method of recognizing characters by constructing a dataset that was robust with various fonts and environmental changes using a large amount of data. Moreover, a coreset capable of evaluating an effective deep learning model and a base set using n-pick sampling capable of responding to a continuously increasing dataset were proposed. Existing original data and the EfficientNet B0 model showed an accuracy of 97.741%. However, the accuracy of our proposed model was increased to 98.274% for the coreset of 8000 images per class. In particular, the accuracy was 98.921% for the base set with only 1900 images per class. MDPI 2021-04-21 /pmc/articles/PMC8122424/ /pubmed/33919360 http://dx.doi.org/10.3390/s21092921 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gang, Sumyung Fabrice, Ndayishimiye Chung, Daewon Lee, Joonjae Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning |
title | Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning |
title_full | Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning |
title_fullStr | Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning |
title_full_unstemmed | Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning |
title_short | Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning |
title_sort | character recognition of components mounted on printed circuit board using deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122424/ https://www.ncbi.nlm.nih.gov/pubmed/33919360 http://dx.doi.org/10.3390/s21092921 |
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