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 |
| Publisher | Springer Verlag |
| Pages | 604-613 |
| Number of pages | 10 |
| ISBN (Print) | 9783319938028 |
| DOIs | |
| Publication status | Published - Jan 1 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 | China |
| City | Shanghai |
| Period | 6/17/18 → 6/22/18 |
Fingerprint
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)
Cite this
Design tool of deep convolutional neural network for visual inspection. / Nagata, Fusaomi; Tokuno, Kenta; Otsuka, Akimasa; Ikeda, Takeshi; Ochi, Hiroaki; Tamano, Hisami; Nakamura, Hitoshi; Watanabe, Keigo; Habib, Maki K.
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); Vol. 10943 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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.
PY - 2018/1/1
Y1 - 2018/1/1
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
PB - Springer Verlag
ER -