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Paper Abstract and Keywords
Presentation 2020-12-17 15:25
Visual inspection system with a small number of anomalous data using DevNet
Katsuhisa Kitaguchi, Yohei Nishizaki, Mamoru Saito (ORIST) PRMU2020-47
Abstract (in Japanese) (See Japanese page) 
(in English) A good visual inspection using deep learning needs to collect a large amount of anomalous data. To solve this problem, we considered applying DevNet, which detects anomalies using few anomaly data, to automatic visual inspection. An experiment to identify scratch data sets of industrial products was conducted using the proposed method. Our method shows higher discrimination performance than a conventional data augmentation method.
Keyword (in Japanese) (See Japanese page) 
(in English) CNN / Visual inspection / Metal parts / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 300, PRMU2020-47, pp. 53-57, Dec. 2020.
Paper # PRMU2020-47 
Date of Issue 2020-12-10 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF PRMU2020-47

Conference Information
Committee PRMU  
Conference Date 2020-12-17 - 2020-12-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Transfer learning and few shot learning 
Paper Information
Registration To PRMU 
Conference Code 2020-12-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Visual inspection system with a small number of anomalous data using DevNet 
Sub Title (in English)  
Keyword(1) CNN  
Keyword(2) Visual inspection  
Keyword(3) Metal parts  
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1st Author's Name Katsuhisa Kitaguchi  
1st Author's Affiliation Osaka Research Institute of Industrial Science and Technology (ORIST)
2nd Author's Name Yohei Nishizaki  
2nd Author's Affiliation Osaka Research Institute of Industrial Science and Technology (ORIST)
3rd Author's Name Mamoru Saito  
3rd Author's Affiliation Osaka Research Institute of Industrial Science and Technology (ORIST)
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Speaker Author-1 
Date Time 2020-12-17 15:25:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2020-47 
Volume (vol) vol.120 
Number (no) no.300 
Page pp.53-57 
#Pages 5 
Date of Issue 2020-12-10 (PRMU) 

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