| Paper Abstract and Keywords |
| Presentation |
2020-02-27 14:15
A note on detection of distress regions in subway tunnels by using U-net based network
An Wang, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
This paper presents an automated distress region detection method using subway tunnel images. We previously proposed a method for realizing distress detection in subway tunnels by using several kinds of fully convolutional networks, namely, FCN, U-net, Seg-net, Residual U-net, and Deeplab v3+. In our previous investigation, we found that the U-net got the highest performance in the subway tunnel distress detection task. However, this original U-net approach had several limitations in its network architecture for the task of distress region detection. In this paper, we attempt to improve the detection performance of U-net by using the ASPP (Atrous Spatial Pyramid Pooling) module from Deeplab v3+network and remain theVGG-16 backbone rather than using ResNet backbone. By introducing this new architecture, we achieve higher performance than conventional methods. We verify the effectiveness of our method through experiments. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Deep learning / semantic segmentaion / U-net / distress detection / subway tunnel image / Atrous spatial pyramid pooling module / / |
| Reference Info. |
ITE Tech. Rep., vol. 44, no. 6, ME2020-42, pp. 69-72, Feb. 2020. |
| Paper # |
ME2020-42 |
| Date of Issue |
2020-02-20 (MMS, HI, ME, AIT) |
| ISSN |
Print edition: ISSN 1342-6893 Online edition: ISSN 2424-1970 |
| Download PDF |
|
| Conference Information |
| Committee |
HI IEICE-IE IEICE-ITS MMS ME AIT |
| Conference Date |
2020-02-27 - 2020-02-28 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Hokkaido Univ. |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
Image Processing, etc. |
| Paper Information |
| Registration To |
ME |
| Conference Code |
2020-02-HI-IE-ITS-MMS-ME-AIT |
| Language |
English |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
A note on detection of distress regions in subway tunnels by using U-net based network |
| Sub Title (in English) |
|
| Keyword(1) |
Deep learning |
| Keyword(2) |
semantic segmentaion |
| Keyword(3) |
U-net |
| Keyword(4) |
distress detection |
| Keyword(5) |
subway tunnel image |
| Keyword(6) |
Atrous spatial pyramid pooling module |
| Keyword(7) |
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| Keyword(8) |
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| 1st Author's Name |
An Wang |
| 1st Author's Affiliation |
Hokkaido University (Hokkaido Univ) |
| 2nd Author's Name |
Ren Togo |
| 2nd Author's Affiliation |
Hokkaido University (Hokkaido Univ) |
| 3rd Author's Name |
Takahiro Ogawa |
| 3rd Author's Affiliation |
Hokkaido University (Hokkaido Univ) |
| 4th Author's Name |
Miki Haseyama |
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Hokkaido University (Hokkaido Univ) |
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| Speaker |
Author-1 |
| Date Time |
2020-02-27 14:15:00 |
| Presentation Time |
15 minutes |
| Registration for |
ME |
| Paper # |
MMS2020-14, HI2020-14, ME2020-42, AIT2020-14 |
| Volume (vol) |
vol.44 |
| Number (no) |
no.6 |
| Page |
pp.69-72 |
| #Pages |
4 |
| Date of Issue |
2020-02-20 (MMS, HI, ME, AIT) |