Improvement of identification performance by effective use of unlabeled radar images for buried objects identification using ground penetrating radar
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- KIMOTO Tomoyuki
- National Institute of Technology, Oita College
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- SONODA Jun
- National Institute of Technology, Sendai College
Bibliographic Information
- Other Title
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- 地中レーダによる埋設物識別においてラベルの無いレーダ画像を有効利用した識別性能の改善
Abstract
<p>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.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2020 (0), 2O5GS1304-2O5GS1304, 2020
The Japanese Society for Artificial Intelligence
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Keywords
Details 詳細情報について
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- CRID
- 1390285300166103296
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- NII Article ID
- 130007857066
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed