Effective automatic defect classification process based on CNN with stacking ensemble model for TFT-LCD panel

Myeongso Kim, Minyoung Lee, Minjeong An, Hongchul Lee

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The classification of defect types during LCD panel production is very important because it is closely related to deciding whether a defect panel is restorable. But since defect areas are very small compared to the panel area, it is hard to classify defect types by images. Therefore, we need to eliminate the background pattern of the panel, but this is not an easy task because the brightness and saturation of the background varies, even in a single image. In this paper, we propose an indicator that can distinguish between defect and background area, which is robust to brightness change and minor noises. With this indicator, we got useful defect information and images with patterns eliminated to make a more efficient defect classifier. The convolutional neural network with stacked ensemble techniques played a great role in improving defect classification performance, when various information from image preprocessing was combined.

Original languageEnglish
Pages (from-to)1165-1174
Number of pages10
JournalJournal of Intelligent Manufacturing
Volume31
Issue number5
DOIs
Publication statusPublished - 2020 Jun 1

Keywords

  • Convolutional neural network
  • Defect classification
  • Pattern elimination

ASJC Scopus subject areas

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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