GAN-based Defect Image Generation for Imbalanced Defect Classification of OLED panels

Jeon, Yongmoon; Kim, Haneol; Lee, Hyeona; Jo, Seonghoon; Kim, Jaewon

GAN-based Defect Image Generation for Imbalanced Defect Classification of OLED panels

Files

145-150.pdf (5.27 MB)

Date

2022

Authors

Jeon, Yongmoon
Kim, Haneol
Lee, Hyeona
Jo, Seonghoon
Kim, Jaewon

Publisher

The Eurographics Association

Abstract

Image classification based on neural networks has been widely explored in machine learning and most research have focused on developing more efficient and accurate network models for given image dataset mostly over natural scene. However, industrial image data have different features with natural scene images in shape of target objects, background patterns, and color. Additionally, data imbalance is one of the most challenging problems to degrade classification accuracy for industrial images. This paper proposes a novel GAN-based image generation method to improve classification accuracy for defect images of OLED panels. We validate our method can synthetically generate defect images of OLED panels and classification accuracy can be improved by training minor classes with the generated defect images.

CCS Concepts: Applied computing --> Computer-aided design

        @inproceedings{10.2312:sr.20221164
,
booktitle = {Eurographics Symposium on Rendering
},
editor = {Ghosh, Abhijeet and 
Wei, Li-Yi
},
title = {{GAN-based Defect Image Generation for Imbalanced Defect Classification of OLED panels
}},
author = {Jeon, Yongmoon and 
Kim, Haneol and 
Lee, Hyeona and 
Jo, Seonghoon and 
Kim, Jaewon
},
year = {2022
},
publisher = {The Eurographics Association
},
ISSN = {1727-3463
},
ISBN = {978-3-03868-187-8
},
DOI = {10.2312/sr.20221164
}
}