Article ; Online: Analysis of Training Deep Learning Models for PCB Defect Detection.
2023 Volume 23, Issue 5
Abstract: 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 ... ...
| Abstract | 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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| MeSH term(s) | Commerce ; Data Accuracy ; Deep Learning ; Drug Contamination ; Industry |
| Language | English |
| Publishing date | 2023-03-02 |
| Publishing country | Switzerland |
| Document type | Journal Article |
| ZDB-ID | 2052857-7 |
| ISSN | 1424-8220 ; 1424-8220 |
| ISSN (online) | 1424-8220 |
| ISSN | 1424-8220 |
| DOI | 10.3390/s23052766 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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