Crack Identification Via User Feedback, Convolutional Neural Networks and Laser Scanners for Tunnel Infrastructures

Eftychios Protopapadakis, Konstantinos Makantasis, George Kopsiaftis, Nikolaos Doulamis, Angelos Amditis

Abstract

In this paper, a deep learning approach synergetically to a laser scanning process are employed for the visual detection and accurate description of concrete defects in tunnels. Analysis is performed over raw RGB images; Convolutional Neural Network serves as the crack detector, during the inspection. In case of a positive detection, the tunnel’s cross-section morphology is assessed via 3D point clouds, created by a laser scanner, allowing the identification of deformations in the compartment. The proposed approach, in contrast to the existing ones, emphasizes on applicability (easy initialization, no preprocessing of the input data) and provides a holistic assessment of the structure; reconstructed 3D model allows the fast identification of structural divergence from the original design, alerting the engineers for possible dangers.

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Paper Citation


in Harvard Style

Protopapadakis E., Makantasis K., Kopsiaftis G., Doulamis N. and Amditis A. (2016). Crack Identification Via User Feedback, Convolutional Neural Networks and Laser Scanners for Tunnel Infrastructures . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: RGB-SpectralImaging, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 725-734. DOI: 10.5220/0005853007250734


in Bibtex Style

@conference{rgb-spectralimaging16,
author={Eftychios Protopapadakis and Konstantinos Makantasis and George Kopsiaftis and Nikolaos Doulamis and Angelos Amditis},
title={Crack Identification Via User Feedback, Convolutional Neural Networks and Laser Scanners for Tunnel Infrastructures},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: RGB-SpectralImaging, (VISIGRAPP 2016)},
year={2016},
pages={725-734},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005853007250734},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: RGB-SpectralImaging, (VISIGRAPP 2016)
TI - Crack Identification Via User Feedback, Convolutional Neural Networks and Laser Scanners for Tunnel Infrastructures
SN - 978-989-758-175-5
AU - Protopapadakis E.
AU - Makantasis K.
AU - Kopsiaftis G.
AU - Doulamis N.
AU - Amditis A.
PY - 2016
SP - 725
EP - 734
DO - 10.5220/0005853007250734