Design Application of Deep Convolutional Neural Network for Vision-Based Defect Inspection

Fusaomi Nagata; Kenta Tokuno; Keigo Watanabe; Maki K. Habib

doi:10.1109/SMC.2018.00295

Design Application of Deep Convolutional Neural Network for Vision-Based Defect Inspection

Fusaomi Nagata, Kenta Tokuno, Keigo Watanabe, Maki K. Habib

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

2 Citations (Scopus)

Abstract

In this decade, deep convolutional neural network called DCNN has been attracting attention due to its high ability of image Recognition and other applications. In this paper, a design application of DCNN is considered and developed for vision-based defect inspection. As a trial test, three kinds of DCNNs are designed, implemented and tested to inspect small defects, such as, crack, burr, protrusion, chipping and spot phenomena seen in the manufacturing process of resin molded articles. An image generator is also implemented to systematically generate range of relevant deformed version of similar images for training. The designed DCNNs are trained using the generated images and then evaluated through classification experiments. The usefulness of the proposed DCNN design application is demonstrated and discussed.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1705-1710
Number of pages	6
ISBN (Electronic)	9781538666500
DOIs	https://doi.org/10.1109/SMC.2018.00295
Publication status	Published - Jan 16 2019
Event	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan Duration: Oct 7 2018 → Oct 10 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference	2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Country	Japan
City	Miyazaki
Period	10/7/18 → 10/10/18

Keywords

additional training
deep convolutional neural network
design application
similar image generator
vision-based defect inspection

ASJC Scopus subject areas

Information Systems
Information Systems and Management
Health Informatics
Artificial Intelligence
Computer Networks and Communications
Human-Computer Interaction

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

Access to Document

10.1109/SMC.2018.00295

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Nagata, F., Tokuno, K., Watanabe, K., & Habib, M. K. (2019). Design Application of Deep Convolutional Neural Network for Vision-Based Defect Inspection. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 (pp. 1705-1710). [8616291] (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2018.00295

Design Application of Deep Convolutional Neural Network for Vision-Based Defect Inspection. / Nagata, Fusaomi; Tokuno, Kenta; Watanabe, Keigo; Habib, Maki K.

Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1705-1710 8616291 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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

Nagata, F, Tokuno, K, Watanabe, K & Habib, MK 2019, Design Application of Deep Convolutional Neural Network for Vision-Based Defect Inspection. in Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018., 8616291, Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1705-1710, 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, Miyazaki, Japan, 10/7/18. https://doi.org/10.1109/SMC.2018.00295

Nagata F, Tokuno K, Watanabe K, Habib MK. Design Application of Deep Convolutional Neural Network for Vision-Based Defect Inspection. In Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1705-1710. 8616291. (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018). https://doi.org/10.1109/SMC.2018.00295

Nagata, Fusaomi ; Tokuno, Kenta ; Watanabe, Keigo ; Habib, Maki K. / Design Application of Deep Convolutional Neural Network for Vision-Based Defect Inspection. Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1705-1710 (Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018).

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abstract = "In this decade, deep convolutional neural network called DCNN has been attracting attention due to its high ability of image Recognition and other applications. In this paper, a design application of DCNN is considered and developed for vision-based defect inspection. As a trial test, three kinds of DCNNs are designed, implemented and tested to inspect small defects, such as, crack, burr, protrusion, chipping and spot phenomena seen in the manufacturing process of resin molded articles. An image generator is also implemented to systematically generate range of relevant deformed version of similar images for training. The designed DCNNs are trained using the generated images and then evaluated through classification experiments. The usefulness of the proposed DCNN design application is demonstrated and discussed.",

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note = "Funding Information: VI. CONCLUSIONS In this paper, a user-friendly DCNN design application is implemented and tested. The application does not require the technical knowledge about programming languages such as Python, C++, Visual Studio, etc. Three types of DCNNs for two, five and six classifications are experimentally designed and trained using the application to inspect small defects such as crack, burr, protrusion, chipping and spot, so that the usefulness and effectiveness of the developed application are confirmed. An additional training method to cope with not well trained samples is shown and evaluated, so that the classification ability can be efficiently and pinpointedly improved to a desired level of categorization accuracy. The proposed DCNN design application is planned to be applied to actual physical inspection processes. ACKNOWLEDGMENT This work was supported by JSPS KAKENHI Grant Number 16K06203.; 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 ; Conference date: 07-10-2018 Through 10-10-2018",

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Design Application of Deep Convolutional Neural Network for Vision-Based Defect Inspection

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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