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Paper Abstract and Keywords
Presentation 2017-01-26 15:20
Object Identification by Ground Penetrating Radar using Deep Learning with Radar Images using FDTD Method on GPU Cluster
Jun Sonoda (NIT, Sendai College), Tomoyuki Kimoto (NIT, Oita College) SANE2016-103
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, deteriorations of social infrastructures such as tunnels and bridges become a serious social problem. It is required to properly and rapidly 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 material and size of the underground object from the radar image obtained the GPR. In this study, to objectively and quantitatively inspect from the GPR images by the deep learning, %for a social infrastructure using the GPR, we have automatically and massively generated the GPR images by a fast finite-difference time-domain (FDTD) simulation on graphics processing units (GPUs), and have learned the underground object using the generated GPR images by a deep convolutional neural network (CNN). It is shown that we have obtained five layers CNN can identify five materials and size with roughly 80 % accuracy in in-homogeneous underground media.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep learning / convolutional neural network / ground penetrating radar / FDTD method / GPU / object identification / /  
Reference Info. IEICE Tech. Rep., vol. 116, no. 427, SANE2016-103, pp. 41-46, Jan. 2017.
Paper # SANE2016-103 
Date of Issue 2017-01-19 (SANE) 
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)
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Conference Information
Committee SANE  
Conference Date 2017-01-26 - 2017-01-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Nagasaki Prefectural Art Museum 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Positioning, navigation, Radar and general 
Paper Information
Registration To SANE 
Conference Code 2017-01-SANE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Object Identification by Ground Penetrating Radar using Deep Learning with Radar Images using FDTD Method on GPU Cluster 
Sub Title (in English)  
Keyword(1) Deep learning  
Keyword(2) convolutional neural network  
Keyword(3) ground penetrating radar  
Keyword(4) FDTD method  
Keyword(5) GPU  
Keyword(6) object identification  
Keyword(7)  
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 2017-01-26 15:20:00 
Presentation Time 25 minutes 
Registration for SANE 
Paper # SANE2016-103 
Volume (vol) vol.116 
Number (no) no.427 
Page pp.41-46 
#Pages 6 
Date of Issue 2017-01-19 (SANE) 

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