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Presentation 2018-09-06 16:05
Clutter Reduction from GPR Image by Deep Learning Using Generative Adversarial Network
Jun Sonoda (NIT, Sendai College), Tomoyuki Kimoto (NIT, Oita College) EST2018-51 Link to ES Tech. Rep. Archives: EST2018-51
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, deterioration of social infrastructures such as tunnels and bridges become a serious social problem. It is required to rapidly and accurately detect for abnormal parts of the social infrastructures. The ground penetrating radar (GPR) is efficient for the social infrastructure inspection. However, it is difficult to identify the material and size of the underground object from the radar image obtained the GPR. To objectively and quantitatively investigate from the GPR images by the deep learning, we have automatically and massively generated the GPR images by a fast finite-difference time-domain (FDTD) simulation with graphics processing units (GPUs), and it has been learned the underground object using a deep convolutional neural network (CNN), with the generated GPR images. As the results, we have obtained multilayer layers CNN can identify six materials and size with roughly more than 80% accuracy in some inhomogeneous underground. In this study, to increase the accuracy of object identification for the GPR images with many clutters in some inhomogeneous underground, we try to reduce clutters from the GPR images by the deep learning using the generative adversarial networks (GAN).
Keyword (in Japanese) (See Japanese page) 
(in English) generative adversarial networks / deep learning / ground penetrating radar / clutter reducing / FDTD method / GPU / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 209, EST2018-51, pp. 47-51, Sept. 2018.
Paper # EST2018-51 
Date of Issue 2018-08-30 (EST) 
ISSN Print edition: ISSN 0913-5685    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 EST2018-51 Link to ES Tech. Rep. Archives: EST2018-51

Conference Information
Committee EST  
Conference Date 2018-09-06 - 2018-09-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Kumejima-machi, Okinawa 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Simulation techniques, etc. 
Paper Information
Registration To EST 
Conference Code 2018-09-EST 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Clutter Reduction from GPR Image by Deep Learning Using Generative Adversarial Network 
Sub Title (in English)  
Keyword(1) generative adversarial networks  
Keyword(2) deep learning  
Keyword(3) ground penetrating radar  
Keyword(4) clutter reducing  
Keyword(5) FDTD method  
Keyword(6) GPU  
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Keyword(8)  
1st Author's Name Jun Sonoda  
1st Author's Affiliation National Institute of Technology, Sendai College (NIT, Sendai College)
2nd Author's Name Tomoyuki Kimoto  
2nd Author's Affiliation National Institute of Technology, Oita College (NIT, Oita College)
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Speaker Author-1 
Date Time 2018-09-06 16:05:00 
Presentation Time 25 minutes 
Registration for EST 
Paper # EST2018-51 
Volume (vol) vol.118 
Number (no) no.209 
Page pp.47-51 
#Pages
Date of Issue 2018-08-30 (EST) 


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