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DC Field | Value | Language |
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dc.contributor.author | Makantasis, Konstantinos | - |
dc.contributor.author | Protopapadakis, Eftychios | - |
dc.contributor.author | Doulamis, Anastasios | - |
dc.contributor.author | Doulamis, Nikolaos | - |
dc.contributor.author | Loupos, Constantinos | - |
dc.date.accessioned | 2024-07-31T08:43:55Z | - |
dc.date.available | 2024-07-31T08:43:55Z | - |
dc.date.issued | 2015-09 | - |
dc.identifier.citation | Makantasis, K., Protopapadakis, E., Doulamis, A., Doulamis, N., & Loupos, C. (2015, September). Deep convolutional neural networks for efficient vision based tunnel inspection. IEEE international conference on intelligent computer communication and processing (ICCP) 2015, Cluj-Napoca. 335-342. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/125118 | - |
dc.description.abstract | The inspection, assessment, maintenance and safe operation of the existing civil infrastructure consists one of the major challenges facing engineers today. Such work requires either manual approaches, which are slow and yield subjective results, or automated approaches, which depend upon complex handcrafted features. Yet, for the latter case, it is rarely known in advance which features are important for the problem at hand. In this paper, we propose a fully automated tunnel assessment approach; using the raw input from a single monocular camera we hierarchically construct complex features, exploiting the advantages of deep learning architectures. Obtained features are used to train an appropriate defect detector. In particular, we exploit a Convolutional Neural Network to construct high-level features and as a detector we choose to use a Multi-Layer Perceptron due to its global function approximation properties. Such an approach achieves very fast predictions due to the feedforward nature of Convolutional Neural Networks and Multi-Layer Perceptrons. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Neural networks (Computer science) | en_GB |
dc.subject | Tunnels -- Testing | en_GB |
dc.subject | Information visualization -- Data processing | en_GB |
dc.subject | Image processing -- Digital techniques | en_GB |
dc.subject | Image analysis -- Data processing | en_GB |
dc.title | Deep convolutional neural networks for efficient vision based tunnel inspection | 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 | 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) | en_GB |
dc.bibliographicCitation.conferenceplace | Cluj-Napoca, Romania, 3-5/09/2015 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/ICCP.2015.7312681 | - |
Appears in Collections: | Scholarly Works - FacICTAI |
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Deep convolutional neural networks for efficient vision based tunnel inspection 2015.pdf Restricted Access | 225.55 kB | Adobe PDF | View/Open Request a copy |
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