A deep learning-based surface defect inspection system for smartphone glass

Gwang Myong Go; Seok Jun Bu; Sung Bae Cho

doi:10.1007/978-3-030-33607-3_41

A deep learning-based surface defect inspection system for smartphone glass

Gwang Myong Go, Seok Jun Bu, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

In recent years, convolutional neural network has become a solution to many image processing problems due to high performance. It is particularly useful for applications in automated optical inspection systems related to industrial applications. This paper proposes a system that combines the defect information, which is meta data, with the defect image by modeling. Our model for classification consists of a separate model for embedding location information in order to utilize the defective locations classified as defective candidates and ensemble with the model for classification to enhance the overall system performance. The proposed system incorporates class activation map for preprocessing and augmentation for image acquisition and classification through optical system, and feedback of classification performance by constructing a system for defect detection. Experiment with real-world dataset shows that the proposed system achieved 97.4% accuracy and through various other experiments, we verified that our system is applicable.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings
Editors	Hujun Yin, Richard Allmendinger, David Camacho, Peter Tino, Antonio J. Tallón-Ballesteros, Ronaldo Menezes
Publisher	Springer
Pages	375-385
Number of pages	11
ISBN (Print)	9783030336066
DOIs	https://doi.org/10.1007/978-3-030-33607-3_41
Publication status	Published - 2019
Event	20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019 - Manchester, United Kingdom Duration: 2019 Nov 14 → 2019 Nov 16

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11871 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019
Country/Territory	United Kingdom
City	Manchester
Period	19/11/14 → 19/11/16

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-33607-3_41

Cite this

Go, G. M., Bu, S. J., & Cho, S. B. (2019). A deep learning-based surface defect inspection system for smartphone glass. In H. Yin, R. Allmendinger, D. Camacho, P. Tino, A. J. Tallón-Ballesteros, & R. Menezes (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings (pp. 375-385). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11871 LNCS). Springer. https://doi.org/10.1007/978-3-030-33607-3_41

Go, Gwang Myong ; Bu, Seok Jun ; Cho, Sung Bae. / A deep learning-based surface defect inspection system for smartphone glass. Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. editor / Hujun Yin ; Richard Allmendinger ; David Camacho ; Peter Tino ; Antonio J. Tallón-Ballesteros ; Ronaldo Menezes. Springer, 2019. pp. 375-385 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In recent years, convolutional neural network has become a solution to many image processing problems due to high performance. It is particularly useful for applications in automated optical inspection systems related to industrial applications. This paper proposes a system that combines the defect information, which is meta data, with the defect image by modeling. Our model for classification consists of a separate model for embedding location information in order to utilize the defective locations classified as defective candidates and ensemble with the model for classification to enhance the overall system performance. The proposed system incorporates class activation map for preprocessing and augmentation for image acquisition and classification through optical system, and feedback of classification performance by constructing a system for defect detection. Experiment with real-world dataset shows that the proposed system achieved 97.4% accuracy and through various other experiments, we verified that our system is applicable.",

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Go, GM, Bu, SJ & Cho, SB 2019, A deep learning-based surface defect inspection system for smartphone glass. in H Yin, R Allmendinger, D Camacho, P Tino, AJ Tallón-Ballesteros & R Menezes (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11871 LNCS, Springer, pp. 375-385, 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, Manchester, United Kingdom, 19/11/14. https://doi.org/10.1007/978-3-030-33607-3_41

A deep learning-based surface defect inspection system for smartphone glass. / Go, Gwang Myong; Bu, Seok Jun; Cho, Sung Bae.
Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. ed. / Hujun Yin; Richard Allmendinger; David Camacho; Peter Tino; Antonio J. Tallón-Ballesteros; Ronaldo Menezes. Springer, 2019. p. 375-385 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11871 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Bu, Seok Jun

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PY - 2019

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N2 - In recent years, convolutional neural network has become a solution to many image processing problems due to high performance. It is particularly useful for applications in automated optical inspection systems related to industrial applications. This paper proposes a system that combines the defect information, which is meta data, with the defect image by modeling. Our model for classification consists of a separate model for embedding location information in order to utilize the defective locations classified as defective candidates and ensemble with the model for classification to enhance the overall system performance. The proposed system incorporates class activation map for preprocessing and augmentation for image acquisition and classification through optical system, and feedback of classification performance by constructing a system for defect detection. Experiment with real-world dataset shows that the proposed system achieved 97.4% accuracy and through various other experiments, we verified that our system is applicable.

AB - In recent years, convolutional neural network has become a solution to many image processing problems due to high performance. It is particularly useful for applications in automated optical inspection systems related to industrial applications. This paper proposes a system that combines the defect information, which is meta data, with the defect image by modeling. Our model for classification consists of a separate model for embedding location information in order to utilize the defective locations classified as defective candidates and ensemble with the model for classification to enhance the overall system performance. The proposed system incorporates class activation map for preprocessing and augmentation for image acquisition and classification through optical system, and feedback of classification performance by constructing a system for defect detection. Experiment with real-world dataset shows that the proposed system achieved 97.4% accuracy and through various other experiments, we verified that our system is applicable.

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Go GM, Bu SJ, Cho SB. A deep learning-based surface defect inspection system for smartphone glass. In Yin H, Allmendinger R, Camacho D, Tino P, Tallón-Ballesteros AJ, Menezes R, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2019 - 20th International Conference, Proceedings. Springer. 2019. p. 375-385. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-33607-3_41

A deep learning-based surface defect inspection system for smartphone glass

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

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