First Step for Vehicle License Plate Identification Using Machine Learning Approach
DOI:
https://doi.org/10.70356/josapen.v1i1.6Keywords:
Machine learning, License plate identification, Neural networks, Transportation optimization, Ethical data handlingAbstract
Automated vehicle license plate identification, critical in modern transportation systems, finds application in traffic monitoring, law enforcement, and transportation optimization. This study explores machine learning's potential to enhance accuracy and efficiency in this domain. Leveraging neural networks and pattern recognition, it aims to build an automated system robust across diverse conditions. Addressing limitations in traditional methods, it focuses on adapting to lighting, angles, and image quality variations. The societal impact includes streamlining law enforcement and optimizing traffic flow, revolutionizing transportation and surveillance. Methodologies cover data collection, ethical considerations, preprocessing, feature extraction, model selection, and iterative refinement. Ethical data handling ensures privacy compliance. Feature extraction methods like HOG, LBP, CNNs, and color histograms capture crucial aspects for identification. Model selection spans SVMs, CNNs, decision trees, and ensemble methods, considering task complexity and dataset characteristics. This study evaluates machine learning's potential for revolutionizing license plate identification systems.
Downloads
References
K. B. Sathya, S. Vasuhi, and V. Vaidehi, “Perspective Vehicle License Plate Transformation using Deep Neural Network on Genesis of CPNet,” Procedia Comput. Sci., vol. 171, pp. 1858–1867, 2020, doi: 10.1016/j.procs.2020.04.199.
W. Puarungroj and N. Boonsirisumpun, “Thai License Plate Recognition Based on Deep Learning,” Procedia Comput. Sci., vol. 135, pp. 214–221, 2018, doi: 10.1016/j.procs.2018.08.168.
D. Neupane, A. Bhattarai, S. Aryal, M. R. Bouadjenek, U. Seok, and J. Seok, “Shine: A deep learning-based accessible parking management system,” Expert Syst. Appl., vol. 238, no. PF, p. 122205, 2024, doi: 10.1016/j.eswa.2023.122205.
M. S. H. Onim et al., “BLPnet: A new DNN model and Bengali OCR engine for Automatic Licence Plate Recognition,” Array, vol. 15, no. March, p. 100244, 2022, doi: 10.1016/j.array.2022.100244.
T. Panchal, H. Patel, and A. Panchal, “License Plate Detection Using Harris Corner and Character Segmentation by Integrated Approach from an Image,” Procedia Comput. Sci., vol. 79, pp. 419–425, 2016, doi: 10.1016/j.procs.2016.03.054.
E. Bentafat, M. M. Rathore, and S. Bakiras, “Towards real-time privacy-preserving video surveillance,” Comput. Commun., vol. 180, no. September, pp. 97–108, 2021, doi: 10.1016/j.comcom.2021.09.009.
L. Zheng, X. He, B. Samali, and L. T. Yang, “An algorithm for accuracy enhancement of license plate recognition,” J. Comput. Syst. Sci., vol. 79, no. 2, pp. 245–255, 2013, doi: 10.1016/j.jcss.2012.05.006.
M. A. Jawale, P. William, A. B. Pawar, and N. Marriwala, “Measurement : Sensors Implementation of number plate detection system for vehicle registration using IOT and recognition using CNN,” Meas. Sensors, vol. 27, no. May, p. 100761, 2023, doi: 10.1016/j.measen.2023.100761.
I. Slimani, A. Zaarane, W. Al Okaishi, I. Atouf, and A. Hamdoun, “An automated license plate detection and recognition system based on wavelet decomposition and CNN,” Array, vol. 8, no. May, p. 100040, 2020, doi: 10.1016/j.array.2020.100040.
P. Fränti and R. Mariescu-Istodor, “Soft precision and recall,” Pattern Recognit. Lett., vol. 167, pp. 115–121, 2023, doi: 10.1016/j.patrec.2023.02.005.
G. Robles, M. R. V. Chaudron, R. Jolak, and R. Hebig, “A reflection on the impact of model mining from GitHub,” Inf. Softw. Technol., vol. 164, no. May, p. 107317, 2023, doi: 10.1016/j.infsof.2023.107317.
D. Deichmann, T. Gillier, and M. Tonellato, “Getting on board with new ideas: An analysis of idea commitments on a crowdsourcing platform,” Res. Policy, vol. 50, no. 9, p. 104320, 2021, doi: 10.1016/j.respol.2021.104320.
M. M. Abdellatif, N. H. Elshabasy, A. E. Elashmawy, and M. AbdelRaheem, “A low cost IoT-based Arabic license plate recognition model for smart parking systems,” Ain Shams Eng. J., vol. 14, no. 6, p. 102178, 2023, doi: 10.1016/j.asej.2023.102178.
O. El Melhaoui and S. Benchaou, “An Efficient Signature Recognition System Based on Gradient Features and Neural Network Classifier,” Procedia Comput. Sci., vol. 198, pp. 385–390, 2021, doi: 10.1016/j.procs.2021.12.258.
T. Agbele, B. Ojeme, and R. Jiang, “Application of local binary patterns and cascade AdaBoost classifier for mice behavioural patterns detection and analysis,” Procedia Comput. Sci., vol. 159, pp. 1375–1386, 2019, doi: 10.1016/j.procs.2019.09.308.
Y. Miao and M. Yamaguchi, “A point selection strategy with edge and line detection for Direct Sparse Visual Odometry,” Graph. Vis. Comput., vol. 6, p. 200051, 2022, doi: 10.1016/j.gvc.2022.200051.
C. Zhou et al., “Multi-scale pseudo labeling for unsupervised deep edge detection,” Knowledge-Based Syst., vol. 280, no. October, p. 111057, 2023, doi: 10.1016/j.knosys.2023.111057.
M. T. T. Cho, A. Chueasamat, T. Hori, H. Saito, and Y. Kohgo, “Effectiveness of filter gabions against slope failure due to heavy rainfall,” Soils Found., vol. 61, no. 2, pp. 480–495, 2021, doi: 10.1016/j.sandf.2021.01.010.
I. Zoppis, G. Mauri, and R. Dondi, Kernel methods: Support vector machines, vol. 1–3. Elsevier Ltd., 2018. doi: 10.1016/B978-0-12-809633-8.20342-7.
A. Gatera, M. Kuradusenge, G. Bajpai, C. Mikeka, and S. Shrivastava, “Comparison of random forest and support vector machine regression models for forecasting road accidents,” Sci. African, vol. 21, p. e01739, 2023, doi: 10.1016/j.sciaf.2023.e01739.
S. Belattar, O. Abdoun, and E. K. Haimoudi, “Performance analysis of the application of convolutional neural networks architectures in the agricultural diagnosis,” Indones. J. Electr. Eng. Comput. Sci., vol. 27, no. 1, pp. 156–162, 2022, doi: 10.11591/ijeecs.v27.i1
Y. Li, E. Herrera-Viedma, G. Kou, and J. A. Morente-Molinera, “Z-number-valued rule-based decision trees,” Inf. Sci. (Ny)., vol. 643, no. May, p. 119252, 2023, doi: 10.1016/j.ins.2023.119252.
M. W. Hasan, “Building an IoT temperature and humidity forecasting model based on long short-term memory (LSTM) with improved whale optimization algorithm,” Memories - Mater. Devices, Circuits Syst., vol. 6, no. October, p. 100086, 2023, doi: 10.1016/j.memori.2023.100086.
L. O. Seman, S. F. Stefenon, V. C. Mariani, and L. dos S. Coelho, “Ensemble learning methods using the Hodrick–Prescott filter for fault forecasting in insulators of the electrical power grids,” Int. J. Electr. Power Energy Syst., vol. 152, no. June, p. 109269, 2023, doi: 10.1016/j.ijepes.2023.109269.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Amirah, Ahmad Sanmorino
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
