|Table of Contents|

License plate number recognition method based on convolution neural network(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2023年4期
Page:
106-117
Research Field:
交通工程
Publishing date:

Info

Title:
License plate number recognition method based on convolution neural network
Author(s):
WANG Shi-fang1;  LI Yu-long1; 2
(1. School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, Anhui, China; 2.School of Information Science and Engineering, Ningbo University, Ningbo 315000, Zhejiang, China)
Keywords:
traffic engineering;  license plate recognition;  LPRNet;  YOLOv5;  license plate location
PACS:
U491.116
DOI:
10.19721/j.cnki.1671-8879.2023.04.011
Abstract:
License plate localization, detection, and recognition in natural road traffic environments are key technologies for implementing intelligent transportation systems. To address the problem of high misrecognition rates and low recognition rates resulting from traditional methods that affected by factors such as failed character segmentation and adverse lighting and large angle inclination, a license plate recognition method was proposed, which combines the YOLOv5(you only look once v5)network with the LPRNet(license plate recognition neural networks)network, CCPD(Chinese city parking dataset)and a self-built dataset were used for training and experiments. First, the YOLOv5 network utilized the CSPNet(cross stage partial network)structure for image feature extraction, and through the fusion of multi-scale feature information, the prediction box was obtained through the CIOU_Loss(complete intersection over union)loss function and the NMS(non-maximum suppression)to locate the position of the license plate. The lightweight LPRNet network did not require character segmentation, but instead used CTC(connectionist temporal classification)to solve the problem of license plate character recognition. To validate the effectiveness of our algorithm, experiments in various scenarios, such as nighttime environments, angle inclination, and rainy, snowy, and foggy weather were conducted. The results show that the average recognition accuracy of the license plate recognition model exceeded 95%, with an average recognition speed of 32 frames/s. Compared with the Easy PR, Hyper LPR, Faster-RCNN+LPRNet, and YOLOv3+LPRNet models, the proposed method improves the recognition accuracy and recall rate, reaching 97.65% and 96.74%, respectively. The improved license plate recognition method exhibites strong robustness even in complex road traffic scenarios and has a significant advantage in recognition speed. This text concludes that the proposed method achieves high accuracy and robustness in license plate recognition, and has a faster recognition speed compared to other models.4 tabs, 15 figs, 24 refs.

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Last Update: 2023-08-20