Abstract
The license plate recognition is an important part of the intelligent traffic management system, and the application of deep learning to the license plate recognition system can effectively improve the speed and accuracy of recognition. Aiming at the problems of traditional license plate recognition algorithms such as the low accuracy, slow speed, and the recognition rate being easily affected by the environment, a Convolutional Neural Network- (CNN-) based license plate recognition algorithm-Fast-LPRNet is proposed. This algorithm uses the nonsegment recognition method, removes the fully connected layer, and reduces the number of parameters. The algorithm—which has strong generalization ability, scalability, and robustness—performs license plate recognition on the FPGA hardware. Increaseing the depth of network on the basis of the Fast-LPRNet structure, the dataset of Chinese City Parking Dataset (CCPD) can be recognized with an accuracy beyond 90%. The experimental results show that the license plate recognition algorithm has high recognition accuracy, strong generalization ability, and good robustness.
Highlights
In recent years, neural networks have been widely studied by researchers
Having studied the target detection algorithms, Safaei found that the training time of Fast-RCNN algorithm detection was reduced by 9.5 h, which opened up a new research path for target detection algorithms [12]. e advantages of the YOLO algorithm have promoted the use of YOLO in the field of license plate recognition systems [13, 14]
In order to verify the feasibility of the improved algorithm, the proposed algorithm is applied to the hardware experimental platform built by FPGA. e platform uses hardware and software cooperation to verify the license plate recognition
Summary
Neural networks have been widely studied by researchers. Among them, the convolutional neural network, which has high adaptability and excellent recognition ability, has been widely used in such fields as classification and recognition and target detection [1,2,3]. E rapid development of deep learning promotes the research process of target detection algorithms. In [4], an adaptive control method based on neural network is proposed to stabilize the air gap of nonlinear maglev train. All these are the combination of neural network and modern transportation system. (i) A license plate recognition algorithm, FASTLPRNET, based on convolutional neural network was proposed (ii) is algorithm can simultaneously complete license plate detection and segmentation-free recognition steps (iii) e neural network hardware environment was successfully deployed on FPGA and the experiment was completed (iv) A large number of experimental results show that this method is a fast and accurate license plate recognition algorithm
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