DOI:
https://doi.org/10.64539/sjer.v2i3.2026.495Keywords:
Automatic Vehicle Number Plate Recognition, AVNPR, Traffic Surveillance, Law Enforcement, Intelligent Transportation SystemAbstract
AVNPR systems are critical in intelligent transportation, monitoring, and law enforcement systems. Nevertheless, the current systems are usually challenged by the issues of dissimilar illumination, obstruction, and the diversity of plate formats, which restrict their practical applicability. To solve these problems, this paper suggests a real-time deep learning-driven AVNPR framework that incorporates effective detection and recognition systems. The proposed system employs the YOLOv8 object detector model to localize number plates with high accuracy and speed, as well as a lightweight recognition module to identify alphanumeric characters. A custom dataset with different types of vehicles in different environmental conditions was created and improved with the help of preprocessing and data augmentation methods to make the model more robust. In the experiments, the proposed system demonstrated an overall system accuracy of 98.7%, representing the combined number plate detection and character recognition results. The mAP@0.5 is 97%, and mAP 0.5-0.95 is 91%, as well as high precision, recall, and F1-score, which suggests that it shows potential applicability across varying conditions in the assessed dataset and suggests that it may be suitable for real-world applications. The system is also implemented with a Flask-based web application, and it supports image based and real-time webcam detection. The results indicate that the proposed framework provides a viable, efficient, and deployable solution to AVNPR applications. The work will lead to the creation of scalable and real-time intelligent transportation systems and give a basis for future advancement in the improvement of robust vehicle recognition in challenging conditions.
References
[1] M. Shawon, S. Molla, M. S. Nowjh, A. E. Emon, and K. Fatema, "Automatic number plate recognition (ANPR) system enhancing security in Bangladesh," Journal of Trends and Challenges in Artificial Intelligence, vol. 3, no. 2, pp. 83-92, 2026. https://jai.aspur.rs/archive/v3/n2/3.pdf.
[2] T. Sampath and R. Kulandaivel, "Advanced Real-Time License Plate Recognition Using Modified Lightweight Convolutional Greylag Goose Network," International Journal of Pattern Recognition and Artificial Intelligence, vol. 40, no. 3, p. 2550038, 2026. https://doi.org/10.1142/S0218001425500387.
[3] O. Bol, B. Kem, and S. Sin, "Automatic Cambodian License Plate Recognition Using Deep Learning With Robust Skew Handling," AIJR Proceedings, vol. 8, no. 1, pp. 22-30, 2026. https://doi.org/10.21467/proceedings.8.1.4.
[4] S. Sravanth, E. Shamitha, M. V. Sai, and Y. Raju, "An Automated Challan Generation System Leveraging Deep Neural Networks for Precise Violation Detection and License Plate Recognition," Digital Cities, pp. 449-460, 2026. https://doi.org/10.1002/9781394233823.ch29.
[5] W. Gata, M. Haris, M. I. Prasetiyowati, and S. Harianto, "EagleEyes: An Artificial Intelligence-Based Approach for Automatic Traffic Violation Detection Using Deep Learning," Journal of Applied Data Sciences, vol. 7, no. 1, pp. 277-289, 2026. https://doi.org/10.47738/jads.v7i1.1097.
[6] M. Z. Hasan and S. G. S. Al-Ali, "Detection and Recognition for Iraqi Modern License Plate Using Deep Learning Approach," Statistics, Optimization and Information Computing, vol. 15, no. 1, pp. 771-786, 2026. https://doi.org/10.19139/soic-2310-5070-2801.
[7] K. R. Singh, R. S. Khedgaonkar, P. M. Sonsare, K. Gupta, and S. S. Badhiye, "Automatic Helmet Detection from Videos Using Artificial Intelligence Method and Engage with Secured Driving System by Message Alert," Iranian Journal of Science and Technology, Transactions of Electrical Engineering, pp. 1-17, 2026. https://doi.org/10.1007/s40998-025-00963-7.
[8] J. Kang et al., "KangNet: Compact Deblurring Model with GAN-Based Blur Translation for License Plate Recognition," Journal of Information Science and Engineering, vol. 42, pp. 213-226, 2026. https://doi.org/10.6688/JISE.202601_42(1).0012.
[9] X. Fan and W. Zhao, "A Robust Multi‐Oriented License Plate Detector and A Derived End‐to‐End License Plate Recognizer," IET Image Processing, vol. 20, no. 1, p. e70272, 2026. https://doi.org/10.1049/ipr2.70272.
[10] X. Huang, H. Zhang, J. Ge, and W. Tang, "YOLO-B2DV: An Integrated Real-Time Framework for Detection, Tracking and Recognition of Catenary Pole Number Plates in Railway Video Streams," IEEE Access, vol. 14, 2026. https://doi.org/10.1109/ACCESS.2026.3653958.
[11] Q. Chen et al., "Automatic Real-Time Queue Length Detection Method of Multiple Lanes at Intersections Based on Roadside LiDAR," Electronics, vol. 15, no. 3, p. 585, 2026. https://doi.org/10.3390/electronics15030585.
[12] G. B. Nardes, T. H. Rausch, B. H. Pereira, D. R. Melo, and C. A. Zeferino, "A resource-constrained CNN accelerator for real-time license plate character recognition on FPGA platforms," Integration, vol. 108, p. 102654, 2026. https://doi.org/10.1016/j.vlsi.2026.102654.
[13] S-L. Lin and C.-W. Li, "Research on Integrating Deep Learning-Based Vehicle Brand and Model Recognition into a Police Intelligence Analysis Platform," Computers, Materials, and Continua, vol. 86, no. 2, p. 1, 2026. https://doi.org/10.32604/cmc.2025.071915.
[14] M. S. Hanif, Z. Nawaz, and M. K. Malik, "Smart Vision Traffic Surveillance: Vehicle Re-Identification and Tracking Using Vision Transformer," Vehicles, vol. 8, no. 2, p. 36, 2026. https://doi.org/10.3390/vehicles8020036.
[15] A. Elouali and A. J. Jara, "Fine-Grained Vehicle Make and Model Recognition for Smart City Environmental Monitoring: A YOLO11-Based Two-Stage Framework," Urban Science, vol. 10, no. 2, p. 74, 2026. https://doi.org/10.3390/urbansci10020074.
[16] B. Narsingoju and R. Jain, "Optimizing Slot Detection by Using Advanced Object Detection Techniques for Intelligent Parking Solutions," Engineering, Technology and Applied Science Research, vol. 16, no. 1, pp. 32298-32304, 2026. https://doi.org/10.48084/etasr.11317.
[17] Y. Jebraeily, Y. Sharafi, M. Teshnehlab, and N. A. Ramezanloo, "An optimized hybrid framework for car theft detection: comparative insights from deep transfer learning and feature-based machine learning," Artificial Intelligence Review, vol. 59, no. 2, p. 68, 2026. https://doi.org/10.1007/s10462-025-11480-8.
[18] M. Chaman, A. El Maliki, Y. Natij, H. Dahou, and A. Hadjoudja, "Deep learning–based real time speed limit sign detection with YOLOv12 on edge AI platforms for embedded ADAS," Bulletin of Electrical Engineering and Informatics, vol. 15, no. 1, pp. 396-405, 2026. https://doi.org/10.11591/eei.v15i1.11128.
[19] Q. Chen, Y. Zhao, X. Luo, W. He, and G. Shi, "Hybrid Deep Learning and Classification Framework for Automatic Traffic Inspection Classification Based on Image Detection," Transactions on Emerging Telecommunications Technologies, vol. 37, no. 2, p. e70325, 2026. https://doi.org/10.1002/ett.70325.
[20] P. Pokorny, S. H. Berge, T. De Jong, C. George, and T. Bjørnskau, "Right-of-way interactions between automated vehicles and other road users in winter conditions–Insights from Oslo, Norway," Transportation Research Part F: Traffic Psychology and Behaviour, vol. 116, p. 103443, 2026. https://doi.org/10.1016/j.trf.2025.103443.
[21] E. Sifuentes, G. Bravo, F. Enríquez-Aguilera, and A. Mejía-Aguilar, "Technologies for Vehicle Classification in Intelligent Transportation Systems: Advances, Challenges, and Perspectives," Decision Support Systems for Industrial and Scientific Applications, pp. 3-22, 2026. https://doi.org/10.1007/978-3-031-89700-9_1.
[22] M. A. Rahmat and A. I. M. Lukman, "Optimizing Lane Detection in Autonomous Vehicles Using Cascading Attention Mechanisms in DeepLabv3+," Engineering, Technology and Applied Science Research, vol. 16, no. 1, pp. 31437-31448, 2026. https://doi.org/10.48084/etasr.15315.
[23] R. Mahum, U. Irshad, A. M. El‐Sherbeeny, M. Shehab, and S. M. Lee, "YOLO‐O: An Improved YOLO‐Based Framework for Vehicle Detection," IET Image Processing, vol. 20, no. 1, p. e70337, 2026. https://doi.org/10.1049/ipr2.70337.
[24] J. Nadaf, A. Kadam, P. Jadhav, P. Kadam, V. Patil, and M. Gaikwad, "Vehicle registration plate recognition using YOLOv8 and IoT," in 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET), 2025, pp. 1-6: IEEE. https://doi.org/10.1109/ICAET63349.2025.10932268.
[25] L. Z. You, B. Z. Jun, P. Y. Han, O. S. Yin, K. W. How, and H. F. San, "Integration of Yolov8 and Paddleocr for car plate recognition system in diverse environments: Malaysia," Journal of Engineering Science and Technology, vol. 20, no. 1, pp. 250-270, 2025. https://jestec.taylors.edu.my/Vol%2020%20Issue%201%20February%202025/20_1_16.pdf.
[26] B. Ramasubramanian, D. Gayathri, K. Priyadharshini, S. Mirdula, P. Sudhakaran, and S. Sabapathy, "Automatic Number Plate Recognition using Vision Transformers and YOLOv8 for ITS," in 2025 6th International Conference on Electronics and Sustainable Communication Systems (ICESC), 2025, pp. 1288-1292. https://doi.org/10.1109/ICESC65114.2025.11212618.
[27] A. Al Noman, N. J. Mila, A. H. Bornil, A. Mazumder, N. Khan, and M. H. I. Bijoy, "BanglaPlate: Automated Vehicle Number Plate Detection and Recognition using YOLOv8," in 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), 2025, pp. 1-6: IEEE. https://doi.org/10.1109/ECCE64574.2025.11013529.
[28] M. Meghana and P. Spurthy, "YOLOv8-Paddle OCR Model for Car Model and Automatic Number Plate Recognition," in 2025 IEEE 4th International Conference for Advancement in Technology (ICONAT), 2025, pp. 1-7. https://doi.org/10.1109/ICONAT66879.2025.11362615.
[29] N. Ruseno, F. Ernawan, M. A. Salamat, M. S. Arshad, and H. Amnur, "An Enhanced YOLOv8 Model Integrated with CNN for Real-Time Vehicle License Plate Recognition," International Journal on Informatics Visualization, vol. 10, no. 1, pp. 343-349, 2026. https://dx.doi.org/10.62527/joiv.10.1.5145.
[30] S. Arukonda, G. S. Jayanth, A. S. S. Koushik, T. Sarupya, P. V. Kumar, and K. B. Reddy, "Real-Time Vehicle Number Plate Recognition and Smart Parking Allocation Using YOLOv8 and OCR for Intelligent Urban Mobility," International Journal of Intelligent Transportation Systems Research, pp. 1-13, 2026. https://doi.org/10.1007/s13177-025-00612-7.
[31] W. Xiong et al., "License plate recognition methodology in complex scenarios based on CSCM-YOLOv8 and CSM-LPRNet," PLoS One, vol. 21, no. 1, p. e0339649, 2026. https://doi.org/10.1371/journal.pone.0339649.
[32] S. Rahman, M. S. Al Ahsan, S. Adhikary, and M. J. Uddin, "License Plate Detection and Recognition for Bangladeshi Vehicles," in 2026 5th International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), 2026, pp. 1-6: IEEE. https://doi.org/10.1109/ICECTE69292.2026.11429408.
[33] V. Gnanaprakash, N. Kanthimathi, and N. Saranya, "Automatic number plate recognition using deep learning," in IOP Conference series: materials science and engineering, 2021, vol. 1084, no. 1, p. 012027: IOP Publishing. http://doi.org/10.1088/1757-899X/1084/1/012027.
[34] M. R. Alam, N. Biswas, and M. Z. Mahmud, "Advanced Traffic Optimization and Automated Toll Collection System using Automated Number Plate Recognition and Modified YOLO Algorithm," in 2025 IEEE 2nd International Conference on Computing, Applications and Systems (COMPAS), 2025, pp. 1-6: IEEE. https://doi.org/10.1109/COMPAS67506.2025.11381723.
[35] S. Basak, A. Khan, J. Tulswani, U. Jangir, and A. Yadav, "Automated Overspeed Vehicle Detection and Number Plate Recognition Using Deep Learning," in 2025 13th International Conference on Intelligent Embedded, MicroElectronics, Communication and Optical Networks (IEMECON), 2025, pp. 1-5: IEEE. https://doi.org/10.1109/IEMECON69302.2025.11365850.
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Copyright (c) 2026 Ahmad Ijaz, Tayyba Sarfraz, Tanzeela Bibi, Muhammad Usman
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