Design tool of deep convolutional neural network for visual inspection

Fusaomi Nagata; Kenta Tokuno; Akimasa Otsuka; Takeshi Ikeda; Hiroaki Ochi; Hisami Tamano; Hitoshi Nakamura; Keigo Watanabe; Maki K. Habib

doi:10.1007/978-3-319-93803-5_57

Design tool of deep convolutional neural network for visual inspection

Fusaomi Nagata, Kenta Tokuno, Akimasa Otsuka, Takeshi Ikeda, Hiroaki Ochi, Hisami Tamano, Hitoshi Nakamura, Keigo Watanabe, Maki K. Habib

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

3 Citations (Scopus)

Abstract

In this paper, a design tool for deep convolutional neural network (DCNN) is considered and developed. As a test trial, a DCNN designed by using the tool is applied to visual inspection system of resin molded articles. The defects to be inspected are crack, burr, protrusion and chipping phenomena that occur in the manufacturing process of resin molded articles. An image generator is also developed to systematically generate many similar images for training. Similar images are easily produced by rotating, translating, scaling and transforming an original image. The designed DCNN is trained using the produced images and is evaluated through classification experiments. The usefulness of the proposed design tool has been confirmed through the test trial.

Original language	English
Title of host publication	Data Mining and Big Data - 3rd International Conference, DMBD 2018, Proceedings
Editors	Yuhui Shi, Qirong Tang, Ying Tan
Publisher	Springer Verlag
Pages	604-613
Number of pages	10
ISBN (Print)	9783319938028
DOIs	https://doi.org/10.1007/978-3-319-93803-5_57
Publication status	Published - 2018
Event	3rd International Conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th International Conference on Swarm Intelligence, ICSI 2018 - Shanghai, China Duration: Jun 17 2018 → Jun 22 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10943 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd International Conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th International Conference on Swarm Intelligence, ICSI 2018
Country/Territory	China
City	Shanghai
Period	6/17/18 → 6/22/18

Keywords

DCNN design tool
Deep convolutional neural network (DCNN)
MATLAB
Training image generator
Visual inspection system

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-93803-5_57

Cite this

Nagata, F., Tokuno, K., Otsuka, A., Ikeda, T., Ochi, H., Tamano, H., Nakamura, H., Watanabe, K., & Habib, M. K. (2018). Design tool of deep convolutional neural network for visual inspection. In Y. Shi, Q. Tang, & Y. Tan (Eds.), Data Mining and Big Data - 3rd International Conference, DMBD 2018, Proceedings (pp. 604-613). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10943 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93803-5_57

Design tool of deep convolutional neural network for visual inspection. / Nagata, Fusaomi; Tokuno, Kenta; Otsuka, Akimasa et al.

Data Mining and Big Data - 3rd International Conference, DMBD 2018, Proceedings. ed. / Yuhui Shi; Qirong Tang; Ying Tan. Springer Verlag, 2018. p. 604-613 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10943 LNCS).

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

Nagata, F, Tokuno, K, Otsuka, A, Ikeda, T, Ochi, H, Tamano, H, Nakamura, H, Watanabe, K & Habib, MK 2018, Design tool of deep convolutional neural network for visual inspection. in Y Shi, Q Tang & Y Tan (eds), Data Mining and Big Data - 3rd International Conference, DMBD 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10943 LNCS, Springer Verlag, pp. 604-613, 3rd International Conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th International Conference on Swarm Intelligence, ICSI 2018, Shanghai, China, 6/17/18. https://doi.org/10.1007/978-3-319-93803-5_57

Nagata F, Tokuno K, Otsuka A, Ikeda T, Ochi H, Tamano H et al. Design tool of deep convolutional neural network for visual inspection. In Shi Y, Tang Q, Tan Y, editors, Data Mining and Big Data - 3rd International Conference, DMBD 2018, Proceedings. Springer Verlag. 2018. p. 604-613. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-93803-5_57

Nagata, Fusaomi ; Tokuno, Kenta ; Otsuka, Akimasa et al. / Design tool of deep convolutional neural network for visual inspection. Data Mining and Big Data - 3rd International Conference, DMBD 2018, Proceedings. editor / Yuhui Shi ; Qirong Tang ; Ying Tan. Springer Verlag, 2018. pp. 604-613 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c84fab2951e748159e425177d7c087fe,

title = "Design tool of deep convolutional neural network for visual inspection",

abstract = "In this paper, a design tool for deep convolutional neural network (DCNN) is considered and developed. As a test trial, a DCNN designed by using the tool is applied to visual inspection system of resin molded articles. The defects to be inspected are crack, burr, protrusion and chipping phenomena that occur in the manufacturing process of resin molded articles. An image generator is also developed to systematically generate many similar images for training. Similar images are easily produced by rotating, translating, scaling and transforming an original image. The designed DCNN is trained using the produced images and is evaluated through classification experiments. The usefulness of the proposed design tool has been confirmed through the test trial.",

keywords = "DCNN design tool, Deep convolutional neural network (DCNN), MATLAB, Training image generator, Visual inspection system",

author = "Fusaomi Nagata and Kenta Tokuno and Akimasa Otsuka and Takeshi Ikeda and Hiroaki Ochi and Hisami Tamano and Hitoshi Nakamura and Keigo Watanabe and Habib, {Maki K.}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 3rd International Conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th International Conference on Swarm Intelligence, ICSI 2018 ; Conference date: 17-06-2018 Through 22-06-2018",

year = "2018",

doi = "10.1007/978-3-319-93803-5_57",

language = "English",

isbn = "9783319938028",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "604--613",

editor = "Yuhui Shi and Qirong Tang and Ying Tan",

booktitle = "Data Mining and Big Data - 3rd International Conference, DMBD 2018, Proceedings",

}

TY - GEN

T1 - Design tool of deep convolutional neural network for visual inspection

AU - Nagata, Fusaomi

AU - Tokuno, Kenta

AU - Otsuka, Akimasa

AU - Ikeda, Takeshi

AU - Ochi, Hiroaki

AU - Tamano, Hisami

AU - Nakamura, Hitoshi

AU - Watanabe, Keigo

AU - Habib, Maki K.

N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

PY - 2018

Y1 - 2018

N2 - In this paper, a design tool for deep convolutional neural network (DCNN) is considered and developed. As a test trial, a DCNN designed by using the tool is applied to visual inspection system of resin molded articles. The defects to be inspected are crack, burr, protrusion and chipping phenomena that occur in the manufacturing process of resin molded articles. An image generator is also developed to systematically generate many similar images for training. Similar images are easily produced by rotating, translating, scaling and transforming an original image. The designed DCNN is trained using the produced images and is evaluated through classification experiments. The usefulness of the proposed design tool has been confirmed through the test trial.

AB - In this paper, a design tool for deep convolutional neural network (DCNN) is considered and developed. As a test trial, a DCNN designed by using the tool is applied to visual inspection system of resin molded articles. The defects to be inspected are crack, burr, protrusion and chipping phenomena that occur in the manufacturing process of resin molded articles. An image generator is also developed to systematically generate many similar images for training. Similar images are easily produced by rotating, translating, scaling and transforming an original image. The designed DCNN is trained using the produced images and is evaluated through classification experiments. The usefulness of the proposed design tool has been confirmed through the test trial.

KW - DCNN design tool

KW - Deep convolutional neural network (DCNN)

KW - MATLAB

KW - Training image generator

KW - Visual inspection system

UR - http://www.scopus.com/inward/record.url?scp=85049007628&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049007628&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-93803-5_57

DO - 10.1007/978-3-319-93803-5_57

M3 - Conference contribution

AN - SCOPUS:85049007628

SN - 9783319938028

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 604

EP - 613

BT - Data Mining and Big Data - 3rd International Conference, DMBD 2018, Proceedings

A2 - Shi, Yuhui

A2 - Tang, Qirong

A2 - Tan, Ying

PB - Springer Verlag

T2 - 3rd International Conference on Data Mining and Big Data, DMBD 2018 held in conjunction with the 9th International Conference on Swarm Intelligence, ICSI 2018

Y2 - 17 June 2018 through 22 June 2018

ER -

Design tool of deep convolutional neural network for visual inspection

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this