JSAI2020

Presentation information

General Session

General Session » J-13 AI application

[2O5-GS-13] AI application: Civil Engineering

Wed. Jun 10, 2020 3:50 PM - 5:30 PM Room O (jsai2020online-15)

座長:曽我真人(和歌山大学)

4:50 PM - 5:10 PM

[2O5-GS-13-04] Improvement of identification performance by effective use of unlabeled radar images for buried objects identification using ground penetrating radar

〇Tomoyuki Kimoto1, Jun Sonoda2 (1. National Institute of Technology, Oita College, 2. National Institute of Technology, Sendai College)

Keywords:Ground penetrating radar, Social infrastructure maintenance, Unsupervised learning, Variational auto encoder: VAE

The our purpose is to develop a system for identify whether it is a risk factor or not from ground penetrating radar images of underground objects. In order to train the CNN whether a radar image is a risk factor, a large number of radar images with risk factor labels required, but in reality, a large number of unlabeled radar images and only a few labeled radar images can be obtained. In this study, we report that the recognition rate can be improved by perform the supervised learning a small number of labeled radar images with MLP after the unsupervised learning of a large number of unlabeled radar images with VAE.

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