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Eftychios Protopapadakis

Researcher at National Technical University of Athens

Publications -  102
Citations -  3700

Eftychios Protopapadakis is an academic researcher from National Technical University of Athens. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 20, co-authored 84 publications receiving 1990 citations. Previous affiliations of Eftychios Protopapadakis include University of the West & Technical University of Crete.

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Journal ArticleDOI

Deep Learning for Computer Vision: A Brief Review.

TL;DR: A brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders are provided.
Proceedings ArticleDOI

Deep Convolutional Neural Networks for efficient vision based tunnel inspection

TL;DR: A fully automated tunnel assessment approach is proposed; using the raw input from a single monocular camera the authors hierarchically construct complex features, exploiting the advantages of deep learning architectures, and achieves very fast predictions due to the feedforward nature of Convolutional Neural Networks and Multi-Layer Perceptrons.
Journal ArticleDOI

Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing

TL;DR: 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, and suggest a promising potential as a driver of autonomous concrete-lining tunnel-inspection robots.
Proceedings ArticleDOI

Smart parking solutions for urban areas

TL;DR: A survey on the needs of drivers from parking infrastructures from a smart services perspective is presented and the latest trends in parking availability monitoring, parking reservation and dynamic pricing schemes are discussed.
Proceedings ArticleDOI

Bayesian-optimized Bidirectional LSTM Regression Model for Non-intrusive Load Monitoring

TL;DR: A Bayesian-optimized bidirectional Long Short -Term Memory (LSTM) method for energy disaggregation, which is structured in a modular way to address multi-dimensionality issues that arise when the number of appliances increase.