IMPLEMENTATION OF IMAGE PROCESSING IN THE RECOGNITION OF OFFICIAL VEHICLE LICENSE PLATES
Abstract
Vehicle license plates are identifiers used to uniquely identify vehicles. However, to identify vehicle license plates there are several problems encountered, namely the different formats of vehicle license plates that make license plate recognition more complicated, vehicle license plates often contain visually similar combinations of letters and numbers (for example the letter "O" and the number "0" or the letter "I" and the number "1"), . in poor lighting conditions license plates may not be clearly visible. To solve this problem, image recognition, image processing, and pattern recognition technologies can be used. The three technologies can be used to recognize characters on vehicle license plates, but cannot yet be used to recognize the colors contained on vehicle license plates. The purpose of this research is to identify and record vehicle license plate numbers quickly and accurately, monitor the presence of vehicles in a supervised area, assist in managing parking, reduce the need for human interaction in the vehicle identification process, The methods used to recognize motor vehicle plates are edge detection and character segmentation which involves image processing to detect the edges of the vehicle plate, followed by segmentation of individual characters in the plate. Another method used is optical character recognition which involves using an optical sensor to take an image of a vehicle plate, then using character recognition techniques to identify the numbers and letters on the plate. The result of this research is that the motor vehicle number recognition system can work in various lighting conditions and poor weather conditions and can monitor and control vehicles in the parking area. The finding obtained from this research is that no method has been used for color recognition on motor vehicle plates.
Downloads
References
Lubna, N. Mufti, and S. A. A. Shah, “Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms,” Sensors 2021, Vol. 21, Page 3028, vol. 21, no. 9, p. 3028, Apr. 2021, doi: 10.3390/S21093028.
S. Ghory and H. Ghafory, “The impact of modern technology in the teaching and learning process,” Int. J. Innov. Res. Sci. Stud., vol. 4, no. 3, pp. 168–173, Jun. 2021, doi: 10.53894/IJIRSS.V4I3.73.
N. S. Arden, A. C. Fisher, K. Tyner, L. X. Yu, S. L. Lee, and M. Kopcha, “Industry 4.0 for pharmaceutical manufacturing: Preparing for the smart factories of the future,” Int. J. Pharm., vol. 602, p. 120554, Jun. 2021, doi: 10.1016/J.IJPHARM.2021.120554.
N. He and G. Li, “Urban neighbourhood environment assessment based on street view image processing: A review of research trends,” Environ. Challenges, vol. 4, p. 100090, Aug. 2021, doi: 10.1016/J.ENVC.2021.100090.
L. F. Li, X. Wang, W. J. Hu, N. N. Xiong, Y. X. Du, and B. S. Li, “Deep Learning in Skin Disease Image Recognition: A Review,” IEEE Access, vol. 8, pp. 208264–208280, 2020, doi: 10.1109/ACCESS.2020.3037258.
G. Fragapane, R. de Koster, F. Sgarbossa, and J. O. Strandhagen, “Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda,” Eur. J. Oper. Res., vol. 294, no. 2, pp. 405–426, Oct. 2021, doi: 10.1016/J.EJOR.2021.01.019.
B. Setiyono, D. A. Amini, and D. R. Sulistyaningrum, “Number plate recognition on vehicle using YOLO - Darknet,” J. Phys. Conf. Ser., vol. 1821, no. 1, p. 012049, Mar. 2021, doi: 10.1088/1742-6596/1821/1/012049.
M. Won, “Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey,” IEEE Access, vol. 8, pp. 73340–73358, 2020, doi: 10.1109/ACCESS.2020.2987634.
P. M. Daigneault, L. Birch, D. Béland, and S. D. Bélanger, “Taking subnational and regional welfare states seriously: Insights from the Quebec case,” J. Eur. Soc. Policy, vol. 31, no. 2, pp. 239–249, May 2021, doi: 10.1177/0958928721996651.
R. D. Castro-Zunti, J. Yépez, and S. B. Ko, “License plate segmentation and recognition system using deep learning and OpenVINO,” IET Intell. Transp. Syst., vol. 14, no. 2, pp. 119–126, Feb. 2020, doi: 10.1049/IET-ITS.2019.0481.
Y. Dai and J. Liu, “Disentangled Feature Learning Network for Vehicle Re-Identification Action recognition and prediction View project,” In IJCAI, pp. 474-480, 2020, doi: 10.24963/ijcai.2020/66.
Mt. Scholar Amit Kochale, A. Khemariya, and A. Tiwari, “Real Time Automatic Vehicle (License) Recognition Identification System with the Help of Opencv & Easyocr Model,” Int. J. Res. Sci. Technol. Manag., vol. 24, no. III, pp. 10–15, 2021.
J. M. S. V. R. Kumar, B. Sujatha, and N. Leelavathi, “Automatic Vehicle Number Plate Recognition System Using Machine Learning,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1074, no. 1, p. 012012, Feb. 2021, doi: 10.1088/1757-899X/1074/1/012012.
Y. Hou et al., “The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis,” Engineering, vol. 7, no. 6, pp. 845–856, Jun. 2021, doi: 10.1016/J.ENG.2020.07.030.
P. Dabek, J. Szrek, R. Zimroz, and J. Wodecki, “An Automatic Procedure for Overheated Idler Detection in Belt Conveyors Using Fusion of Infrared and RGB Images Acquired during UGV Robot Inspection,” Energies 2022, Vol. 15, Page 601, vol. 15, no. 2, p. 601, Jan. 2022, doi: 10.3390/EN15020601.
E. M. Ugwu, O. E. Taylor, and N. D. Nwiabu, “An Improved Visual Attention Model for Automated Vehicle License Plate Number Recognition Using Computer Vision,” Eur. J. Artif. Intell. Mach. Learn., vol. 1, no. 3, pp. 15–21, May 2022, doi: 10.24018/EJAI.2022.1.3.10.
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, p. 100040, Dec. 2020, doi: 10.1016/J.ARRAY.2020.100040.
K. Zhou, W. Li, and D. Zhao, “Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3+,” Technol. Heal. Care, vol. 30, no. S1, pp. 173–190, Jan. 2022, doi: 10.3233/THC-228017.
P. R. K. Varma, S. Ganta, B. Hari Krishna, and P. Svsrk, “A Novel Method for Indian Vehicle Registration Number Plate Detection and Recognition using Image Processing Techniques,” Procedia Comput. Sci., vol. 167, pp. 2623–2633, Jan. 2020, doi: 10.1016/J.PROCS.2020.03.324.
B. A. Hussain and M. S. Hathal, “Development of Iraqi License Plate Recognition System based on Canny Edge Detection Method,” J. Eng., vol. 26, no. 7, pp. 115–126, 2019, doi: 10.31026/j.eng.2020.07.08.
A. Pal, A. Shukla, and A. Pathak, “An efficient quantum-classical hybrid algorithm for distorted alphanumeric character identification,” Dec. 2022, Accessed: Jun. 18, 2023. [Online]. Available: https://arxiv.org/abs/2212.12861v1
S. Pashine, R. Dixit, and R. Kushwah, “Handwritten Digit Recognition using Machine and Deep Learning Algorithms,” Int. J. Comput. Appl., vol. 176, no. 42, pp. 27–33, Jun. 2021, doi: 10.5120/ijca2020920550.
R. Laroca, L. A. Zanlorensi, G. R. Gonçalves, E. Todt, W. R. Schwartz, and D. Menotti, “An efficient and layout-independent automatic license plate recognition system based on the YOLO detector,” IET Intell. Transp. Syst., vol. 15, no. 4, pp. 483–503, Apr. 2021, doi: 10.1049/ITR2.12030.
A. R. Ortoncelli, M. Marcon, and F. Beal, “An automated approach to mitigate transcription errors in braille texts for the Portuguese language,” pp. 293–300, Mar. 2021, doi: 10.14210/cotb.v12.p293-300.
M. Ghazi Yaseen, S. Salih, M. Aljanabi, A. H. Ali, and S. A. Abd, “Improving Process Efficiency in Iraqi universities: a proposed management information system”, Iraqi Journal For Computer Science and Mathematics 4, no. 1, pp. 211-219, 2023, doi: 10.52866/ijcsm.2023.01.01.0020.
P. Akpojotor, A. Adetunmbi, B. Alese, and A. Oluwatope, “Automatic license plate recognition on microprocessors and custom computing platforms: A review,” IET Image Process., vol. 15, no. 12, pp. 2717–2735, Oct. 2021, doi: 10.1049/IPR2.12262.
Pen Yonkav 4/KC, “Arti Kode Pada Plat Nomor Mobil Dinas TNI-AD,” 2022. https://www.yonkav4.id/2022/01/arti-kode-pada-plat-nomor-mobil-dinas.html (accessed Jul. 15, 2023).
Abstract viewed = 356 times
PDF downloaded = 264 times
Copyright (c) 2023 Santoso Setiawan, Daning Nur Sulistyowati, Nurman Machmud
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
