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(사)한국터널지하공간학회

ISSN : 2233-8292

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Vol.24 No.6
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Deep learning based crack detection from tunnel cement concrete lining

(사)한국터널지하공간학회 / (사)한국터널지하공간학회, (P)2233-8292; (E)2287-4747
2022, v.24 no.6, pp.583-598
https://doi.org/10.9711/KTAJ.2022.24.6.583





& (2022). Deep learning based crack detection from tunnel cement concrete lining. (사)한국터널지하공간학회, 24(6), 583-598, https://doi.org/10.9711/KTAJ.2022.24.6.583
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Abstract

As human-based tunnel inspections are affected by the subjective judgment of the inspector, making continuous history management difficult. There is a lot of deep learning-based automatic crack detection research recently. However, the large public crack datasets used in most studies differ significantly from those in tunnels. Also, additional work is required to build sophisticated crack labels in current tunnel evaluation. Therefore, we present a method to improve crack detection performance by inputting existing datasets into a deep learning model. We evaluate and compare the performance of deep learning models trained by combining existing tunnel datasets, high-quality tunnel datasets, and public crack datasets. As a result, DeepLabv3+ with Cross-Entropy loss function performed best when trained on both public datasets, patchwise classification, and oversampled tunnel datasets. In the future, we expect to contribute to establishing a plan to efficiently utilize the tunnel image acquisition system’s data for deep learning model learning.

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

(사)한국터널지하공간학회

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