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https://www.um.edu.mt/library/oar/handle/123456789/125118
Title: | Deep convolutional neural networks for efficient vision based tunnel inspection |
Authors: | Makantasis, Konstantinos Protopapadakis, Eftychios Doulamis, Anastasios Doulamis, Nikolaos Loupos, Constantinos |
Keywords: | Neural networks (Computer science) Tunnels -- Testing Information visualization -- Data processing Image processing -- Digital techniques Image analysis -- Data processing |
Issue Date: | 2015-09 |
Publisher: | Institute of Electrical and Electronics Engineers |
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. |
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. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/125118 |
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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