A Deep Metric Neural Network with Disentangled Representation for Detecting Smartphone Glass Defects

Gwang Myong Go, Seok Jun Bu, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For defect inspection using computer vision, deep learning models have been introduced to improve the conventional rule-based pattern analysis. A lot of data is a prerequisite to the success of them, but the on-the-spot industrial field suffers from lack of data. In this paper, we propose a deep metric neural network to improve the performance even with insufficient data imbalanced in class. The model is verified with the dataset of new products by evaluating the accuracy with 10-fold cross-validation. Our model is based on the data in the smallest category, 1.2 K, which achieves the highest performance of 90.42% using sampled pairs without using all the data for training. High accuracy has been achieved and proven applicability in the industry compared to the conventional machine learning models.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2020 - 21st International Conference, 2020, Proceedings
EditorsCesar Analide, Paulo Novais, David Camacho, Hujun Yin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages485-494
Number of pages10
ISBN (Print)9783030623647
DOIs
Publication statusPublished - 2020
Event21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020 - Guimaraes, Portugal
Duration: 2020 Nov 42020 Nov 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12490 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020
CountryPortugal
CityGuimaraes
Period20/11/420/11/6

Bibliographical note

Funding Information:
Acknowledgments. This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)) and Samsung Electronics Co., Ltd.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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