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

• Main Authors: Syed Tahir Hussain Rizvi, Denis Patti, Tomas Björklund, Gianpiero Cabodi, Gianluca Francini
• Format: Article
• Language: English
• Published: MDPI AG 2017-10-01
• Series: Future Internet
• Subjects: convolutional neural network; visual analysis; embedded platforms; general purpose GPU; license plate detection
• Online Access: https://www.mdpi.com/1999-5903/9/4/66

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.