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Article

Enhanced YOLOv8-Based System for Automatic Number Plate Recognition

by
Tamim Mahmud Al-Hasan
1,
Victor Bonnefille
2 and
Faycal Bensaali
1,*
1
Department of Electrical Engineering, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar
2
Académie Militaire de Saint-Cyr Coëtquidan, Rue de Saint-Cyr, 56381 Guer, France
*
Author to whom correspondence should be addressed.
Technologies 2024, 12(9), 164; https://doi.org/10.3390/technologies12090164
Submission received: 20 August 2024 / Revised: 4 September 2024 / Accepted: 11 September 2024 / Published: 13 September 2024

Abstract

This paper presents an advanced automatic number plate recognition (ANPR) system designed specifically for Qatar’s diverse license plate landscape and challenging environmental conditions. Leveraging the YOLOv8 deep learning model, particularly the YOLOv8s variant, we achieve state-of-the-art accuracy in both license plate detection and number recognition. Our innovative approach includes a comprehensive dataset enhancement technique that simulates adverse conditions, significantly improving the model’s robustness in real-world scenarios. We integrate edge computing using a Raspberry Pi with server-side processing, demonstrating an efficient solution for real-time ANPR applications. The system maintains greater than 93% overall performance across various environmental conditions, including night-time and rainy scenarios. We also explore the impact of various pre-processing techniques, including edge detection, k-mean thresholding, DBSCAN, and Gaussian mixture models, on the ANPR system’s performance. Our findings indicate that modern deep learning models like YOLOv8 are sufficiently robust to handle raw input images and do not significantly benefit from additional pre-processing. With its high accuracy and real-time processing capability, the proposed system represents a significant advancement in ANPR technology and is particularly suited for Qatar’s unique traffic management needs and smart city initiatives.
Keywords: ANPR systems; computer vision; image processing; machine learning; Qatari license plates; YOLOv8 ANPR systems; computer vision; image processing; machine learning; Qatari license plates; YOLOv8
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MDPI and ACS Style

Al-Hasan, T.M.; Bonnefille, V.; Bensaali, F. Enhanced YOLOv8-Based System for Automatic Number Plate Recognition. Technologies 2024, 12, 164. https://doi.org/10.3390/technologies12090164

AMA Style

Al-Hasan TM, Bonnefille V, Bensaali F. Enhanced YOLOv8-Based System for Automatic Number Plate Recognition. Technologies. 2024; 12(9):164. https://doi.org/10.3390/technologies12090164

Chicago/Turabian Style

Al-Hasan, Tamim Mahmud, Victor Bonnefille, and Faycal Bensaali. 2024. "Enhanced YOLOv8-Based System for Automatic Number Plate Recognition" Technologies 12, no. 9: 164. https://doi.org/10.3390/technologies12090164

APA Style

Al-Hasan, T. M., Bonnefille, V., & Bensaali, F. (2024). Enhanced YOLOv8-Based System for Automatic Number Plate Recognition. Technologies, 12(9), 164. https://doi.org/10.3390/technologies12090164

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