Deep Classifiers-Based License Plate Detection, Localization and Recognition on GPU-Powered Mobile Platform
AbstractThe 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
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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.
Rizvi STH, 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(4):66.Chicago/Turabian Style
Rizvi, Syed T.H.; Patti, Denis; Björklund, Tomas; Cabodi, Gianpiero; Francini, Gianluca. 2017. "Deep Classifiers-Based License Plate Detection, Localization and Recognition on GPU-Powered Mobile Platform." Future Internet 9, no. 4: 66.
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