Abstract
In this paper, a crack detection mechanism for concrete tunnel surfaces is presented. The proposed methodology leverages deep Convolutional Neural Networks and domain-specific heuristic post-processing techniques to address a variety of challenges, including high accuracy requirements, low operational times and limited hardware resources, poor and variable lighting conditions, low textured lining surfaces, scarcity of training data, and abundance of noise. The proposed framework leverages the representational power of the convolutional layers of CNNs, which inherently selects effective features, thus obviating the need for the tedious task of handcrafted feature extraction. Additionally, the good performance rates attained by the proposed framework are acquired at a significantly lower execution time compared to other techniques. The presented mechanism was designed and developed as a core component of an autonomous robotic inspector deployed and validated in the tunnels of Egnatia Motorway in Metsovo, Greece. The obtained results denote the proposed approach’s superiority over a variety of methods and suggest a promising potential as a driver of autonomous concrete-lining tunnel-inspection robots.
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Acknowledgements
The authors would like to thank all members of the ROBO-SPECT project for their collaboration and support throughout the project lifetime. This research is partly implemented through IKY scholarships programme and co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the action titled “Reinforcement of Postdoctoral Researchers”, in the framework of the Operational Programme “Human Resources Development Program, Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) 2014 – 2020.
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Protopapadakis, E., Voulodimos, A., Doulamis, A. et al. Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing. Appl Intell 49, 2793–2806 (2019). https://doi.org/10.1007/s10489-018-01396-y
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DOI: https://doi.org/10.1007/s10489-018-01396-y