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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Distress Detection in Subway Tunnel Images via Data Augmentation Based on Selective Image Cropping and Patching
| Title: | Distress Detection in Subway Tunnel Images via Data Augmentation Based on Selective Image Cropping and Patching |
| Authors: | Maeda, Keisuke Browse this author | | Takada, Saya Browse this author | | Haruyama, Tomoki Browse this author | | Togo, Ren Browse this author | | Ogawa, Takahiro Browse this author →KAKEN DB | | Haseyama, Miki Browse this author →KAKEN DB |
| Keywords: | deep learning | | distress detection | | data augmentation | | subway tunnels | | maintenance |
| Issue Date: | 18-Nov-2022 |
| Publisher: | MDPI |
| Journal Title: | Sensors |
| Volume: | 22 |
| Issue: | 22 |
| Start Page: | 8932 |
| Publisher DOI: | 10.3390/s22228932 |
| Abstract: | Distresses, such as cracks, directly reflect the structural integrity of subway tunnels. Therefore, the detection of subway tunnel distress is an essential task in tunnel structure maintenance. This paper presents the performance improvement of deep learning-based distress detection to support the maintenance of subway tunnels through a new data augmentation method, selective image cropping and patching (SICAP). Specifically, we generate effective data for training the distress detection model by focusing on the distressed regions via SICAP. After the data augmentation, we train a distress detection model using the expanded training data. The new image generated based on SICAP does not change the pixel values of the original image. Thus, there is little loss of information, and the generated images are effective in constructing a robust model for various subway tunnel lines. We conducted experiments with some comparative methods. The experimental results show that the detection performance can be improved by our data augmentation. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/87629 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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