| 講演抄録／キーワード |
| 講演名 |
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） |
| 抄録 |
（和） |
(まだ登録されていません) |
| （英） |
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. |
| キーワード |
（和） |
/ / / / / / / |
| （英） |
Deep learning / semantic segmentaion / U-net / distress detection / subway tunnel image / Atrous spatial pyramid pooling module / / |
| 文献情報 |
映情学技報, vol. 44, no. 6, ME2020-42, pp. 69-72, 2020年2月. |
| 資料番号 |
ME2020-42 |
| 発行日 |
2020-02-20 (MMS, HI, ME, AIT) |
| ISSN |
Print edition: ISSN 1342-6893 Online edition: ISSN 2424-1970 |
| PDFダウンロード |
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