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Paper Abstract and Keywords
Presentation 2020-12-18 14:40
Construction of SSD model applied Feature Contraction and Rand Augment by small training data
Tomokazu Ozawa (UNICO), Yuki Matsumoto, Katsushi Miura (SEI), Takuya Okuno (SCE) PRMU2020-60
Abstract (in Japanese) (See Japanese page) 
(in English) Appearance inspection is often performed to maintain quality in the industrial production. Until now, the appearance inspection has been performed by visual confirming. However, there is a problem that the result of appearance inspection differs by inspectors. In recent years, the automation of appearance inspection has become heated by introducing AI. In this research, we try to apply the SSD which is one of object detection algorisms using AI technologies, to appearance inspection in situ. In the learning of the SSD, it needs some images of good and defective products. The images of defective products, however, must artificially be made and the number of defective images that can be made limited, since the occurrence of detective products is extremely few in actual process. In this study, therefore, we show that we can construct high performance SSD models with small training data by introducing Feature Contraction and Rand Augment to the learning of the SSD.
Keyword (in Japanese) (See Japanese page) 
(in English) Feature Contraction / Rand Augment / SSD / mobileNetV1 / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 300, PRMU2020-60, pp. 128-132, Dec. 2020.
Paper # PRMU2020-60 
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)
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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) Construction of SSD model applied Feature Contraction and Rand Augment by small training data 
Sub Title (in English)  
Keyword(1) Feature Contraction  
Keyword(2) Rand Augment  
Keyword(3) SSD  
Keyword(4) mobileNetV1  
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Keyword(6)  
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1st Author's Name Tomokazu Ozawa  
1st Author's Affiliation UNICO Co.,Ltd. (UNICO)
2nd Author's Name Yuki Matsumoto  
2nd Author's Affiliation Sumitomo Electric Industries, Ltd. (SEI)
3rd Author's Name Katsushi Miura  
3rd Author's Affiliation Sumitomo Electric Industries, Ltd. (SEI)
4th Author's Name Takuya Okuno  
4th Author's Affiliation Sumiden Communication Engineering Co.,Ltd. (SCE)
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Speaker Author-1 
Date Time 2020-12-18 14:40:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2020-60 
Volume (vol) vol.120 
Number (no) no.300 
Page pp.128-132 
#Pages 5 
Date of Issue 2020-12-10 (PRMU) 

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