| 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 |
| Download PDF |
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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. |
| Keyword(6) |
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| Keyword(7) |
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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 |
Author-1 |
| Date Time |
2021-02-18 10:20:00 |
| Presentation Time |
25 minutes |
| Registration for |
ME |
| Paper # |
MMS2021-1, ME2021-1, AIT2021-1 |
| Volume (vol) |
vol.45 |
| Number (no) |
no.4 |
| Page |
pp.1-6 |
| #Pages |
6 |
| Date of Issue |
2021-02-11 (MMS, ME, AIT) |