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(사)한국터널지하공간학회

ISSN : 2233-8292

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Vol.23 No.6

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Training a semantic segmentation model for cracks in the concrete lining of tunnel

(사)한국터널지하공간학회 / (사)한국터널지하공간학회, (P)2233-8292; (E)2287-4747
2021, v.23 no.6, pp.549-558
https://doi.org/10.9711/KTAJ.2021.23.6.549






& (2021). Training a semantic segmentation model for cracks in the concrete lining of tunnel. (사)한국터널지하공간학회, 23(6), 549-558, https://doi.org/10.9711/KTAJ.2021.23.6.549

Abstract

In order to keep infrastructures such as tunnels and underground facilities safe, cracks of concrete lining in tunnel should be detected by regular inspections. Since regular inspections are accomplished through manual efforts using maintenance lift vehicles, it brings about traffic jam, exposes works to dangerous circumstances, and deteriorates consistency of crack inspection data. This study aims to provide methodology to automatically extract cracks from tunnel concrete lining images generated by the existing tunnel image acquisition system. Specifically, we train a deep learning based semantic segmentation model with open dataset, and evaluate its performance with the dataset from the existing tunnel image acquisition system. In particular, we compare the model performance in case of using all of a public dataset, subset of the public dataset which are related to tunnel surfaces, and the tunnel-related subset with negative examples. As a result, the model trained using the tunnel-related subset with negative examples reached the best performance. In the future, we expect that this research can be used for planning efficient model training strategy for crack detection.

keywords
딥러닝, 균열탐지, 의미론적 분할, 영상처리, 콘크리트 라이닝, Deep learning, Crack detection, Semantic segmentation, Image processing, Concrete lining

(사)한국터널지하공간학회

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