IMPROVEMENT OF STIV TECHNIQUE BY USING DEEP LEARNING
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- FUJITA Ichiro
- 神戸大学 一般財団法人建設工学研究所
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- WATANABE Ken
- 株式会社ハイドロ総合技術研究所
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- IGUCHI Makiko
- 株式会社ハイドロ総合技術研究所
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- HASEGAWA Makoto
- 株式会社ハイドロ総合技術研究所
Bibliographic Information
- Other Title
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- 深層学習を用いたSTIV解析の高性能化
Abstract
<p> With the flood disasters that have been occurring frequently in recent years, the sophistication of discharge observation has been promoted. Since the existing river monitoring cameras can be used, interest in image measurement methods is increasing. Among the image measurement methods, the STIV method is regarded as a powerful method because of its high measurement accuracy and robustness of measurement. However, depending on the shooting conditions such as rough weather, there are cases in which the conventional automatic analysis method produces an erroneous value, which is a challenge for establishing a real-time measurement system. Therefore, in this study, we tried to solve these problems by incorporating the deep learning method, which has been successful in the field of image analysis in recent years, into the STIV method. As a result, it was clarified that STIV method using deep learning is able to obtain correct results in many cases where the conventional method outputs anomalous values.</p>
Journal
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- Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering)
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Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 76 (2), I_991-I_996, 2020
Japan Society of Civil Engineers
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Details 詳細情報について
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- CRID
- 1390290229666824576
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- NII Article ID
- 130008122704
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- ISSN
- 2185467X
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed