An Efficient Methodology of Automatic Vehicle Number Plate Detection Using Deep Learning
Keywords:
ANPR, Character Segmentation, Convolutional Neural Networks, Edge Detection, License Plate Extraction, Morphology, OCRAbstract
Using advanced computer vision techniques, Vehicle Number Recognition (VNR) can determine a vehicle's unique identifier in real-time video. An efficient Vehicle Number Recognition System will be developed and implemented to facilitate the automated collection of toll taxes. The device will first try to determine what kind of automobile it is before snapping a photo of the front of the vehicle. Localization and partitioning of characters on a vehicle's license plate. The system works best with monochrome images, but it can still decipher the license plate's color. The effectiveness of the system is evaluated using real-world photos and videos once it has been constructed and simulated using deep learning and other technologies. Additionally, the vehicle data (including the date, time, and toll amount) is tracked by the database. Features have been extracted and classified using deep learning. For experimental analysis, we employed both synthetic datasets and real-time photographs of car registration numbers. Data Acquisition and pre-processing methods including color space conversion, cropping, filtering for noise reduction and enhancement are all carried out using the suggested framework. The histogram segmentation technique of picture segmentation is carried out via several feature extraction selection strategies. Classification in deep learning is used to address issues with many hidden layers and unique optimization strategies. Ultimately, the system's effectiveness is demonstrated by contrasting the suggested system with other cutting-edge techniques and algorithms. The outcomes of the trial demonstrate that the system's design can correctly recognize an automobile's license plate in both stationary and moving images.
Downloads
References
Shashidhar, R., et al. "Vehicle Number Plate Detection and Recognition using YOLO-V3 and OCR Method." 2021 IEEE International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2021.
Al Awaimri, Mohammed, et al. "Vehicles Number Plate Recognition Systems A Systematic Review." 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, 2021.
Maheswari, V. Uma, Rajanikanth Aluvalu, and Swapna Mudrakola. "An integrated number plate recognition system through images using threshold-based methods and KNN." 2022 International Conference on Decision Aid Sciences and Applications (DASA). IEEE, 2022.
Mir, Md Nazmul Hossain, et al. "IoT based digital toll collection system: A perspective." 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE, 2021.
Lubna, Naveed Mufti, and Syed Afaq Ali Shah. "Automatic number plate Recognition: A detailed survey of relevant algorithms." Sensors 21.9 (2021): 3028.
Rajput, Sudhir Kumar, et al. "Automatic Vehicle Identification and Classification Model Using the YOLOv3 Algorithm for a Toll Management System." Sustainability 14.15 (2022): 9163.
Alam, Nur-A., et al. "Intelligent system for vehicles number plate detection and recognition using convolutional neural networks." Technologies 9.1 (2021): 9.
Ahmed, Ahmed Abdelmoamen, and Sheikh Ahmed. "A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition." Algorithms 14.11 (2021): 317.
Álvarez-Bazo, Fernando, et al. "A low-cost automatic vehicle identification sensor for traffic networks analysis." Sensors 20.19 (2020): 5589.
Kumar, JMSV Ravi, B. Sujatha, and N. Leelavathi. "Automatic vehicle number plate recognition system using machine learning." IOP Conference Series: Materials Science and Engineering. Vol. 1074. No. 1. IOP Publishing, 2021.
G. Kumar, A. Barman, M. Pal, “License Plate Tracking using Gradient based Segmentation,” IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2019-Octob, 1737–1740,2019, doi:10.1109/TENCON.2019.8929688.
N.O. Yaseen, S.G.S. Al-Ali, A. Sengur, “An Efficient Model for Automatic Number Plate Detection using HOG Feature from New North Iraq Vehicle Images Dataset,” 1st International Informatics and Software Engineering Conference: Innovative Technologies for Digital Transformation, IISEC 2019 - Proceedings, 2019, doi:10.1109/UBMYK48245.2019.8965573.
I.V. Pustokhina, D.A. Pustokhin, J.J.P.C. Rodrigues, D. Gupta, A. Khanna, K. Shankar, C. Seo, G.P. Joshi, “Automatic Vehicle License Plate Recognition Using Optimal K-Means with Convolutional Neural Network for Intelligent Transportation Systems,” IEEE Access, 8, 92907–92917, 2020, doi:10.1109/ACCESS.2020.2993008.
M.Y. Arafat, A.S.M. Khairuddin, R. Paramesran, “Connected component analysis integrated edge based technique for automatic vehicular license plate recognition framework,” IET Intelligent Transport Systems, 14(7), 712–723, 2020, doi:10.1049/iet-its.2019.0006.
R. Laroca, L.A. Zanlorensi, G.R. Gonçalves, E. Todt, W.R. Schwartz, D. Menotti, “An efficient and layout-independent automatic license plate recognition system based on the yolo detector,” ArXiv, 2019.
N. Saleem, H. Muazzam, H.M. Tahir and U. Farooq, “Automatic License Plate Recognition using Extracted Features”, Proceedings of 4th International Symposium on Computational and Business Intelligence, pp. 221-225, 2016.
R. Islam, K.F. Sharif and S. Biswas, “Automatic Vehicle Number Plate Recognition using Structured Elements”, Proceedings of IEEE International Conference on Systems, Process and Control, pp. 44-48, 2015
Puranic, K.T. Deepak and V. Umadevi, “Vehicle Number Plate Recognition System: A Literature Review and Implementation using Template Matching”, International Journal of Computer Applications, Vol. 134, No. 1, pp. 12- 16, 2016.
P. Sai Krishna, “Automatic Number Plate Recognition by using Matlab”, International Journal of Innovative Research in Electronics and Communications, Vol. 2, No. 4, pp. 1-7, 2015.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
