An End-to-End Deep Learning Approach for Plate Recognition in Intelligent Transportation Systems

Abstract

Accurate and fast recognition of license plates is one of the most important challenges in the field of license plate recognition systems. Due to the high frame rate of surveillance cameras, old license plate recognition systems cannot be used in real-time applications. On the other hand, the presence of natural and artificial noise and different light and weather conditions make the detection and recognition process of these systems challenging. In this paper, an end-to-end method for efficiently detecting and recognizing plates is presented. In the proposed method, vehicles are first detected using a single-shot detector- (SSD-) based deep learning model in the video frames and the input images. This will increase the speed and accuracy in identifying the location of the plate in the given images. Then, the location of the plate is identified using the proposed architecture based on convolutional networks. Finally, using a deep convolutional network and long short-term memory (LSTM), the characters related to the plate are recognized. An advantage of our method is that the proposed deep network is trained using different images with different qualities that leads to high performance in detecting and recognizing plates. Also, considering that in the proposed method the vehicles are first detected and then the plate is detected in the vehicle image, there is no limit in the number of identified plates. Moreover, plate detection in the vehicle rectangle, instead of the whole frame, speeds up our method. The proposed method is evaluated using several databases. The first part of the evaluation focuses on robustness and recognition speed. The proposed method has the accuracy of 100% for vehicle detection, 100% for plate detection, and 99.37% for character recognition. In the second part of evaluation, the proposed method is evaluated in terms of overall speed. The experimental results witness that the proposed method is capable of processing 30 frames per second without losing any data and also outperforms several methods proposed in recent years, in terms of time and accuracy.