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Future Internet 2017, 9(4), 66; https://doi.org/10.3390/fi9040066

Deep Classifiers-Based License Plate Detection, Localization and Recognition on GPU-Powered Mobile Platform

1
Dipartimento di Automatica e Informatica (DAUIN), Politecnico di Torino, 10129 Turin, Italy
2
Dipartimento di Elettronica (DET), Politecnico di Torino, 10129 Turin, Italy
3
Joint Open Lab, Telecom Italia Mobile (TIM), 10129 Turin, Italy
*
Authors to whom correspondence should be addressed.
Received: 14 September 2017 / Revised: 17 October 2017 / Accepted: 18 October 2017 / Published: 21 October 2017
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Abstract

The realization of a deep neural architecture on a mobile platform is challenging, but can open up a number of possibilities for visual analysis applications. A neural network can be realized on a mobile platform by exploiting the computational power of the embedded GPU and simplifying the flow of a neural architecture trained on the desktop workstation or a GPU server. This paper presents an embedded platform-based Italian license plate detection and recognition system using deep neural classifiers. In this work, trained parameters of a highly precise automatic license plate recognition (ALPR) system are imported and used to replicate the same neural classifiers on a Nvidia Shield K1 tablet. A CUDA-based framework is used to realize these neural networks. The flow of the trained architecture is simplified to perform the license plate recognition in real-time. Results show that the tasks of plate and character detection and localization can be performed in real-time on a mobile platform by simplifying the flow of the trained architecture. However, the accuracy of the simplified architecture would be decreased accordingly. View Full-Text
Keywords: convolutional neural network; visual analysis; embedded platforms; general purpose GPU; license plate detection convolutional neural network; visual analysis; embedded platforms; general purpose GPU; license plate detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Rizvi, S.T.H.; Patti, D.; Björklund, T.; Cabodi, G.; Francini, G. Deep Classifiers-Based License Plate Detection, Localization and Recognition on GPU-Powered Mobile Platform. Future Internet 2017, 9, 66.

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