| Presentation | 2020-01-30 Embedded object identification from ground penetrating radar image by semi-supervised learning using variational auto-encoder Tomoyuki Kimoto, Jun Sonoda, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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 e?cient for the social infrastructure inspection, but, it is di?cult 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 ?nite-di?erence 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) |
| Keyword(in English) | Grand Penetrating Rader / Deep Learning / Variational Auto Encoder / Semi-Supervised Leraning |
| Paper # | EST2019-80 |
| Date of Issue | 2020-01-23 (EST) |
| Conference Information | |
| Committee | EST |
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| Conference Date | 2020/1/30(2days) |
| 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. |
| Chair | Akimasa Hirata(Nagoya Inst. of Tech.) |
| Vice Chair | Shinichiro Ohnuki(Nihon Univ.) / Masayuki Kimishima(Advantest) / Jun Shibayama(Hosei Univ.) |
| Secretary | Shinichiro Ohnuki(National Inst. of Tech.,Sendai College) / Masayuki Kimishima(Aoyama Gakuin Univ.) / Jun Shibayama |
| Assistant | Takahiro Ito(Nagoya Inst. of Tech.) / Kazuhiro Fujita(Fujitsu) |
| Paper Information | |
| Registration To | Technical Committee on Electronics Simulation Technology |
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| Language | JPN |
| 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 |
| 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) |
| Date | 2020-01-30 |
| Paper # | EST2019-80 |
| Volume (vol) | vol.119 |
| Number (no) | EST-407 |
| Page | pp.pp.7-12(EST), |
| #Pages | 6 |
| Date of Issue | 2020-01-23 (EST) |