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| Presentation | 2020-01-30 09:40 Embedded object identification from ground penetrating radar image by semi-supervised learning using variational auto-encoder Tomoyuki Kimoto (NIT, Oita), Jun Sonoda (NIT, Sendai)  EST2019-80 Link to ES Tech. Rep. Archives: EST2019-80 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recently, deterioration of social infrastructures such as tunnels and bridges becomes 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, but, it is difficult to identify the material and size of the underground object from the radar image obtained the GPR. In our previous studies, we have massively generated the GPR images by a fast finite-difference time-domain (FDTD) simulation, we make learned the generated GPR images to the convolution neural network (CNN). As the results, it has been clarified that the relative permittivity and size of the object can be identified from the underground radar images. However, when using real images of construction sites, correct labels such as the relative permittivity of buried objects can only be examined by digging the ground, and supervised learning is not practical. In this study, we use the unsupervised learning of radar images using variational auto encoders (VAE), and mapping high-dimensional information of radar images to latent space. And we report that the identification rate is improved, by semi-supervised learning to give correct labels to some of these latent variables. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Grand Penetrating Rader / Deep Learning / Variational Auto Encoder / Semi-Supervised Leraning / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 407, EST2019-80, pp. 7-12, Jan. 2020. | |
| Paper # | EST2019-80 | |
| Date of Issue | 2020-01-23 (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 | EST2019-80 Link to ES Tech. Rep. Archives: EST2019-80 |
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| Conference Information | |
| Committee | EST |
| Conference Date | 2020-01-30 - 2020-01-31 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Beppu International Convention Center |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Simulation Technique, etc. |
| Paper Information | |
| Registration To | EST |
| Conference Code | 2020-01-EST |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Embedded object identification from ground penetrating radar image by semi-supervised learning using variational auto-encoder |
| Sub Title (in English) | |
| Keyword(1) | Grand Penetrating Rader |
| Keyword(2) | Deep Learning |
| Keyword(3) | Variational Auto Encoder |
| Keyword(4) | Semi-Supervised Leraning |
| Keyword(5) | |
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| 1st Author's Name | Tomoyuki Kimoto |
| 1st Author's Affiliation | National Institute of Technology, Oita College (NIT, Oita) |
| 2nd Author's Name | Jun Sonoda |
| 2nd Author's Affiliation | National Institute of Technology, Sendai College (NIT, Sendai) |
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| Speaker | Author-1 |
| Date Time | 2020-01-30 09:40:00 |
| Presentation Time | 25 minutes |
| Registration for | EST |
| Paper # | EST2019-80 |
| Volume (vol) | vol.119 |
| Number (no) | no.407 |
| Page | pp.7-12 |
| #Pages | 6 |
| Date of Issue | 2020-01-23 (EST) |
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