Earticle

초록

영어

A tunnel is an essential public facility that enables uninterrupted transportation in crowded cities. Over time, various factors such as ageing and harsh environment could slowly damage the tunnel, leading to cracks and even human loss. There, the tunnel needs to be investigated regularly. Previous maintenance methods have primarily counted on the operators who directly monitor recorded videos to inspect the cracks and determine their seriousness. However, this is a time-consuming and error-prone process. Firstly, this paper introduces a huge tunnel cracks segmentation dataset that contains a total of 170,339 images. Next, a tunnel crack segmentation system that can automatically identify different types of cracks is suggested based on the collected data. The model uses the U-Net structure as the baseline model, with the encoder replaced by a pre-trained Resnet-152 model to improve the effectiveness of the feature extract process. Finally, additional measurements of the detected cracks, such as crack length and crack thickness, are computed.

목차

Abstract
1. Introduction
2. Dataset
3. Methodology
3.1 Deep learning-based crack segmentation
3.2 Post-processing and skeletonization
3.3 Crack measurements
4. Experiment result
5. Conclusions
Acknowledgement
References

저자

• L. Minh Dang [ Department of Information and Communication Engineering, and Convergence Engineering for Intelligent Drone, Sejong University ]
• Chanmi Oh [ Department of Computer Science and Engineering, Sejong University ]
• Yanfen Li [ Department of Computer Science and Engineering, Sejong University ]
• Hanxiang Wang [ Department of Computer Science and Engineering, Sejong University ]
• Chang-Jae Chun [ Department of Computer Science and Engineering, Sejong University ]
• Hyeonjoon Moon [ Department of Computer Science and Engineering, Sejong University ] Corresponding author

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