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Paper Abstract and Keywords
Presentation 2021-02-18 10:20
A Note on Improving Performance of Deep Learning-based Distress Detection for Supporting Maintenance of Subway Tunnels -- Accuracy Verification Focusing on Tunnel Wall Characteristics --
Tomoki Haruyama, Keisuke Maeda, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents the performance improvement of deep learning-based distress detection to support the maintenance of subway tunnels.
Specifically, the detection performance is verified by focusing on the characteristics of subway tunnels where the distress images were taken.
In addition, this paper analyzes the effect of the number of distress images used as training data on the detection performance.
As a result of the above analysis, it is confirmed that the detection performance can be improved by using the distress images obtained from the wall surface with the same characteristics between training and test data.
Finally, it was confirmed that as the number of defect images used as training data increased, the detection performance was performed with high accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep learning / Distress detection / Data augmentation / Subway tunnels / Maintenance. / / /  
Reference Info. ITE Tech. Rep., vol. 45, no. 4, ME2021-1, pp. 1-6, Feb. 2021.
Paper # ME2021-1 
Date of Issue 2021-02-11 (MMS, ME, AIT) 
ISSN Print edition: ISSN 1342-6893  Online edition: ISSN 2424-1970
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Conference Information
Committee IEICE-IE IEICE-ITS MMS ME AIT  
Conference Date 2021-02-18 - 2021-02-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To ME 
Conference Code 2021-02-IE-ITS-MMS-ME-AIT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Note on Improving Performance of Deep Learning-based Distress Detection for Supporting Maintenance of Subway Tunnels 
Sub Title (in English) Accuracy Verification Focusing on Tunnel Wall Characteristics 
Keyword(1) Deep learning  
Keyword(2) Distress detection  
Keyword(3) Data augmentation  
Keyword(4) Subway tunnels  
Keyword(5) Maintenance.  
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1st Author's Name Tomoki Haruyama  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Keisuke Maeda  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Ren Togo  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Takahiro Ogawa  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
5th Author's Name Miki Haseyama  
5th Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker
Date Time 2021-02-18 10:20:00 
Presentation Time 25 
Registration for ME 
Paper # ITE-MMS2021-1,ITE-ME2021-1,ITE-AIT2021-1 
Volume (vol) ITE-45 
Number (no) no.4 
Page pp.1-6 
#Pages ITE-6 
Date of Issue ITE-MMS-2021-02-11,ITE-ME-2021-02-11,ITE-AIT-2021-02-11 


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