Methodology for an automatic license plate recognition system using Convolutional Neural Networks for a Peruvian case study
Keywords:
Artificial vision, license plate recognition, Optical Character, vehicleAbstract
In Peru, the number of vehicles increased in the last decade, therefore, the automatic control requires a long database with a specific license plate model. It should be used for registration, evaluation and information extraction associated to the cars to control access parking with a particular license plate characteristic, especially for government buildings, therefore, an automatic evaluation of the license plate should be more dynamic and effective than European systems due to quantity of characters, specific segmentation and additional information in the Peruvian plate. License plate recognition is a widely applied system in artificial vision for the automatic obtaining of car license plates. This research article seeks to design a Peruvian license plate recognition system to reduce the time of vehicle registration and it involves accurate recognition of the plate location and extraction. Image processing techniques are used using the Python programming language, together with the OpenCV library. In addition, with YoloV4 a neural network is trained to locate the area where the license plate is located to facilitate the application of an Optical Character Recognizer (OCR). Our findings are a new improvement and evaluation in the traditional license plate software, with a new Peruvian database, so it allowed extracting the registration information instantly with high accuracy evaluated with 200 to 1000 images; therefore, the new contribution is the improvement in the false positive values with an accuracy of 100%, rate of failure of 0% and the sensibility of 100% with a specificity of 100% (neural network trained with 1000 images); besides it is the database for the future works for Peruvian cars.
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References
M. Peker, "Comparison of Tensorflow Object Detection Networks for Licence Plate Localization," 2019 1st Global Power, Energy and Communication Conference (GPECOM), 2019, pp. 101-105.
Taufiq, et al., "Real-time Vehicle License Plate Detection by Using Convolutional Neural Network Algorithm with Tensorflow," 2018 2nd Borneo International Conference on Applied Mathematics and Engineering (BICAME), 2018, pp. 275-279.
Q. Huang, Z. Cai and T. Lan, "A Single Neural Network for Mixed Style License Plate Detection and Recognition," in IEEE Access, vol. 9, pp. 21777-21785, 2021.
P. Shivakumara, et al., "CNN-RNN based method for license plate recognition," in CAAI Transactions on Intelligence Technology, vol. 3, no. 3, pp. 169-175, 9 2018.
W. Wang, et al., "A Light CNN for End-to-End Car License Plates Detection and Recognition," in IEEE Access, vol. 7, pp. 75-83, 2019.
Jiafan Zhuang, et al., “Towards Human-Level License Plate Recognition” Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 306-32.
Sergio Montazzolli Silva, Claudio Rosito Jung; “License Plate Detection and Recognition in Unconstrained Scenarios”. Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 580-596.
C. Henry, S. Y. Ahn and S. Lee, "Multinational License Plate Recognition Using Generalized Character Sequence Detection," in IEEE Access, vol. 8, pp. 35185-35199, 2020, doi: 10.1109/ACCESS.2020.2974973.
R. R. Palekar, S. U. Parab, D. P. Parikh and V. N. Kamble, "Real time license plate detection using openCV and tesseract," 2017 International Conference on Communication and Signal Processing (ICCSP), 2017, pp. 2111-2115.
B. Martín del Brío and C. Serrano Cinca, “Fundamentos de las redes neuronales artificiales hardware y software”. Scire: Representación y organización del conocimiento, ISSN 1135-3716, Vol. 1, Nº 1, 1995, págs. 103-125, 1995
J.V. Mejía Lara, R.M. Arias Velásquez, "Low-cost image analysis with convolutional neural network for herpes zoster", Biomedical Signal Processing and Control, 71, Part B, 2022, 103250, 1-8.
Redes neuronales convolucionales. (s. f.). MathWorks - Creadores de MATLAB y Simulink - MATLAB y Simulink - MATLAB & Simulink. https://la.mathworks.com/discovery/convolutional-neural-network-matlab.html
P. Pérez and M. Valente. “Fundamentos básicos del procesamiento de imágenes” (2018). FAMAF UNC. https://www.famaf.unc.edu.ar/~pperez1/manuales/cim/cap2.html
L. Rodriguez, et al., “Integración de funciones para Procesamiento Digital de Imágenes en Python”, Universidad Tecnológica de Torreón
OpenCV: Smoothing Images. (2021). OpenCV documentation index. $https://docs.opencv.org/4.5.2/d4/d13/tutorial_py_filtering.html$
OpenCV: Canny Edge Detection. (2021). OpenCV documentation index. $https://docs.opencv.org/3.4/da/d22/tutorial_py_canny.html$
“Github”, 2019. [En línea]. Disponible en: tesseract-ocr.github.io/ . [Accedido: 08-sep-2021]
“Asociación Automotriz del Perú”, 2021. [En línea]. Disponible en: https://aap.org.pe/placas/tipos/ordinarias/tipo-de-placas/ . [Accedido: 01-sep-2021]
Y. Romero, et al., "Temporal and spatial analysis of traffic – Related pollutant under the influence of the seasonality and meteorological variables over an urban city in Peru", Heliyon, 6, 6, 2020, e04029, 1-10.
