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DC Field | Value | Language |
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dc.contributor.author | Protopapadakis, Eftychios | - |
dc.contributor.author | Makantasis, Konstantinos | - |
dc.contributor.author | Kopsiaftis, George | - |
dc.contributor.author | Doulamis, Nikolaos | - |
dc.contributor.author | Amditis, Angelos | - |
dc.date.accessioned | 2024-08-12T11:10:56Z | - |
dc.date.available | 2024-08-12T11:10:56Z | - |
dc.date.issued | 2016-02 | - |
dc.identifier.citation | Protopapadakis, E., Makantasis, K., Kopsiaftis, G., Doulamis, N., & Amditis, A. (2016, February). Crack Identification Via User Feedback, Convolutional Neural Networks and Laser Scanners for Tunnel Infrastructures. 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016), Rome. 725-734. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/125384 | - |
dc.description.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. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | SciTePress | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Concrete -- Cracking -- Detection | en_GB |
dc.subject | Tunnels -- Inspection | en_GB |
dc.subject | Neural networks (Computer science) | en_GB |
dc.subject | Lasers in surveying | en_GB |
dc.subject | Image processing -- Digital techniques | en_GB |
dc.title | Crack identification via user feedback, convolutional neural networks and laser scanners for tunnel infrastructures | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | 11th Joint conference on computer vision, imaging and computer graphics theory and applications - VISIGRAPP 2016 | en_GB |
dc.bibliographicCitation.conferenceplace | Rome, Italy, 27-29/02/2016. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.5220/ 0005853007250734 | - |
Appears in Collections: | Scholarly Works - FacICTAI |
Files in This Item:
File | Description | Size | Format | |
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Crack identification via user feedback, convolutional neural networks and laser scanners for tunnel infrastructures 2016.pdf Restricted Access | 22.22 MB | Adobe PDF | View/Open Request a copy |
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