Fast detection algorithm for cracks on tunnel linings based on deep semantic segmentation

Abstract

An algorithm based on deep semantic segmentation called LC-DeepLab is proposed for detecting the trends and geometries of cracks on tunnel linings at the pixel level. The proposed method addresses the low accuracy of tunnel crack segmentation and the slow detection speed of conventional models in complex backgrounds. The novel algorithm is based on the DeepLabv3+ network framework. A lighter backbone network was used for feature extraction. Next, an efficient shallow feature fusion module that extracts crack features across pixels is designed to improve the edges of crack segmentation. Finally, an efficient attention module that significantly improves the anti-interference ability of the model in complex backgrounds is validated. Four classic semantic segmentation algorithms (fully convolutional network, pyramid scene parsing network, U-Net, and DeepLabv3+) are selected for comparative analysis to verify the effectiveness of the proposed algorithm. The experimental results show that LC-DeepLab can accurately segment and highlight cracks from tunnel linings in complex backgrounds, and the accuracy (mean intersection over union) is 78.26%. The LC-DeepLab can achieve a real-time segmentation of 416 × 416 × 3 defect images with 46.98 f/s and 21.85 Mb parameters.

Graphical abstract

Keywords

tunnel engineering / crack segmentation / fast detection / DeepLabv3+ / feature fusion / attention mechanism

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 50908234, 52208421), the Open Fund of the National Engineering Research Center of Highway Maintenance Technology, Changsha University of Science & Technology (No. kfj220101), the Natural Science Foundation of Hunan Province (No. 2020JJ4743), and the Research Innovation Project for Postgraduate of Central South University (No. 1053320213484).

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