Paper Abstract and Keywords |
Presentation |
2017-07-21 09:50
Characteristics of Object Identification by Ground Penetrating Radar Images using Deep Learning Jun Sonoda (NIT, Sendai), Tomoyuki Kimoto (NIT, Oita) EMT2017-23 MW2017-48 OPE2017-28 EST2017-25 MWP2017-25 Link to ES Tech. Rep. Archives: EMT2017-23 MW2017-48 OPE2017-28 EST2017-25 MWP2017-25 |
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 adequately 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. In this study, to objectively and quantitatively inspect 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 have learned the underground object using the generated GPR images by a deep convolutional neural network (CNN). It is shown that we have obtained multilayer layers CNN can identify six 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. 117, no. 142, EST2017-25, pp. 89-94, July 2017. |
Paper # |
EST2017-25 |
Date of Issue |
2017-07-13 (EMT, MW, OPE, EST, MWP) |
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 |
EMT2017-23 MW2017-48 OPE2017-28 EST2017-25 MWP2017-25 Link to ES Tech. Rep. Archives: EMT2017-23 MW2017-48 OPE2017-28 EST2017-25 MWP2017-25 |
Conference Information |
Committee |
MWP OPE EMT MW EST IEE-EMT |
Conference Date |
2017-07-20 - 2017-07-21 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Obihiro Chamber of Commerce and Industry |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Light wave & Electromagnetic Wave Workshop |
Paper Information |
Registration To |
EST |
Conference Code |
2017-07-MWP-OPE-EMT-MW-EST-EMT |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Characteristics of Object Identification by Ground Penetrating Radar Images using Deep Learning |
Sub Title (in English) |
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Deep learning |
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convolutional neural network |
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ground penetrating radar |
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FDTD method |
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GPU |
Keyword(6) |
object identification |
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1st Author's Name |
Jun Sonoda |
1st Author's Affiliation |
National Institute of Technology, Sendai College (NIT, Sendai) |
2nd Author's Name |
Tomoyuki Kimoto |
2nd Author's Affiliation |
National Institute of Technology, Oita College (NIT, Oita) |
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Speaker |
Author-1 |
Date Time |
2017-07-21 09:50:00 |
Presentation Time |
25 minutes |
Registration for |
EST |
Paper # |
EMT2017-23, MW2017-48, OPE2017-28, EST2017-25, MWP2017-25 |
Volume (vol) |
vol.117 |
Number (no) |
no.139(EMT), no.140(MW), no.141(OPE), no.142(EST), no.143(MWP) |
Page |
pp.89-94 |
#Pages |
6 |
Date of Issue |
2017-07-13 (EMT, MW, OPE, EST, MWP) |
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