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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 >
Defect Detection of Subway Tunnels Using Advanced U-Net Network
| Title: | Defect Detection of Subway Tunnels Using Advanced U-Net Network |
| Authors: | Wang, An 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 | | semantic segmentation | | U-Net | | defect detection | | subway tunnel |
| Issue Date: | 17-Mar-2022 |
| Publisher: | MDPI |
| Journal Title: | Sensors |
| Volume: | 22 |
| Issue: | 6 |
| Start Page: | 2330 |
| Publisher DOI: | 10.3390/s22062330 |
| Abstract: | In this paper, we present a novel defect detection model based on an improved U-Net architecture. As a semantic segmentation task, the defect detection task has the problems of background-foreground imbalance, multi-scale targets, and feature similarity between the background and defects in the real-world data. Conventionally, general convolutional neural network (CNN)-based networks mainly focus on natural image tasks, which are insensitive to the problems in our task. The proposed method has a network design for multi-scale segmentation based on the U-Net architecture including an atrous spatial pyramid pooling (ASPP) module and an inception module, and can detect various types of defects compared to conventional simple CNN-based methods. Through the experiments using a real-world subway tunnel image dataset, the proposed method showed a better performance than that of general semantic segmentation including state-of-the-art methods. Additionally, we showed that our method can achieve excellent detection balance among multi-scale defects. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/85081 |
| 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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