Analysis of Training Deep Learning Models for PCB Defect Detection

Recently, many companies have introduced automated defect detection methods for defect-free PCB manufacturing. In particular, deep learning-based image understanding methods are very widely used. In this study, we present an analysis of training deep learning models to perform PCB defect detection s...

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 23; no. 5; p. 2766
Main Authors Park, Joon-Hyung, Kim, Yeong-Seok, Seo, Hwi, Cho, Yeong-Jun
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 02.03.2023
MDPI
Subjects
Algorithms
Automation
Commerce
Contamination
Data Accuracy
Datasets
Deep Learning
defect inspection
Defects
Drug Contamination
Image quality
Industry
Information management
machine vision
Manufacturing
Neural networks
product inspection
smart factory
smart manufacturing
product inspection
deep learning
smart manufacturing
defect inspection
smart factory
machine vision
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Summary:Recently, many companies have introduced automated defect detection methods for defect-free PCB manufacturing. In particular, deep learning-based image understanding methods are very widely used. In this study, we present an analysis of training deep learning models to perform PCB defect detection stably. To this end, we first summarize the characteristics of industrial images, such as PCB images. Then, the factors that can cause changes (contamination and quality degradation) to the image data in the industrial field are analyzed. Subsequently, we organize defect detection methods that can be applied according to the situation and purpose of PCB defect detection. In addition, we review the characteristics of each method in detail. Our experimental results demonstrated the impact of various degradation factors, such as defect detection methods, data quality, and image contamination. Based on our overview of PCB defect detection and experiment results, we present knowledge and guidelines for correct PCB defect detection.
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These authors contributed equally to this work.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23052766