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Paper Abstract and Keywords
Presentation 2023-03-03 16:20
Classifying Cable Tendency with Semantic Segmentation by Utilizing Real and Simulated RGB Data
Pei-Chun Chien, Powei Liao, Eiji Fukuzawa, Jun Ohya (Waseda Univ.) PRMU2022-117 IBISML2022-124
Abstract (in Japanese) (See Japanese page) 
(in English) Cable tendency is the potential shape or characteristic that a cable may possess while being manipulated during automated production, of which some are considered erroneous and should be identified as a part of anomaly detection. This research explores the ability of deep learning models in learning the cable tendencies that, contrary to typical classification tasks of multi-object scenarios, is to differentiate the multiple states displayable by the same object -- in this case, cables. By training multiple models with different combinations of self-collected real-world data and self-generated simulation data, a comparative study is carried out to compare the performance of each approach. In conclusion, the effectiveness of detecting five abnormal states and shapes of cables, and using simulation data is certificated in experiments.
Keyword (in Japanese) (See Japanese page) 
(in English) deep learning / tendency / anomaly detection / synthetic / simulation data / Blender / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 404, PRMU2022-117, pp. 311-318, March 2023.
Paper # PRMU2022-117 
Date of Issue 2023-02-23 (PRMU, IBISML) 
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 PRMU2022-117 IBISML2022-124

Conference Information
Committee PRMU IBISML IPSJ-CVIM  
Conference Date 2023-03-02 - 2023-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Future University Hakodate 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2023-03-PRMU-IBISML-CVIM 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Classifying Cable Tendency with Semantic Segmentation by Utilizing Real and Simulated RGB Data 
Sub Title (in English)  
Keyword(1) deep learning  
Keyword(2) tendency  
Keyword(3) anomaly detection  
Keyword(4) synthetic  
Keyword(5) simulation data  
Keyword(6) Blender  
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Keyword(8)  
1st Author's Name Pei-Chun Chien  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Powei Liao  
2nd Author's Affiliation Waseda University (Waseda Univ.)
3rd Author's Name Eiji Fukuzawa  
3rd Author's Affiliation Waseda University (Waseda Univ.)
4th Author's Name Jun Ohya  
4th Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2023-03-03 16:20:00 
Presentation Time 10 minutes 
Registration for PRMU 
Paper # PRMU2022-117, IBISML2022-124 
Volume (vol) vol.122 
Number (no) no.404(PRMU), no.405(IBISML) 
Page pp.311-318 
#Pages 8 
Date of Issue 2023-02-23 (PRMU, IBISML) 

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