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 proceedingConference 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 languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1705-1710
Number of pages6
ISBN (Electronic)9781538666500
DOIs
Publication statusPublished - Jan 16 2019
Event2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan
Duration: Oct 7 2018Oct 10 2018

Publication series

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

Conference

Conference2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
CountryJapan
CityMiyazaki
Period10/7/1810/10/18

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

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  • Cite this

    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