Next-Generation License Plate Detection and Recognition System Using YOLOv8

Amin, Arslan; Mumtaz, Rafia; Bashir, Muhammad Jawad; Zaidi, Syed Mohammad Hassan

doi:10.1109/honet59747.2023.10374756

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2024

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Cited by 3 publications

References 16 publications

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A License Plate Recognition Method Based on YOLOv8-Pose and E-LPRNet

Ji,

Shi,

Fan

et al. 2024

2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)

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A License Plate Recognition Method Based on YOLOv8-Pose and E-LPRNet

Ji,

Shi,

Fan

et al. 2024

2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)

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Cost-Sensitive Road Obstacle Detection for Low-Cost Autonomous Vehicles

Su,

Zhao,

Tomioka

et al. 2024

2024 IEEE Intelligent Mobile Computing (MobileCloud)

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Real-time number plate detection using AI and ML

Swathi,

Sai Tejaswi,

Amanulla Khan

et al. 2024

Gamification and Augmented Reality

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The abstract presents a research study focusing on real-time license plate verification, a key feature of electronic systems that operate by rapidly identifying and removing identification numbers from vehicle registration in a dynamic global environment. The research leverages the combination of artificial intelligence (AI) and machine learning (ML) techniques, specifically the integration of region-based convolutional neural networks (RCNN) and advanced RCNN algorithms, to create a powerful and readily available system. In terms of methods, this research optimizes algorithm performance and deploys the system in a cloud-based environment to improve accessibility and scalability. Through careful design and optimization, the proposed system has achieved a consistent result in license recognition, as evident from the well-accounted evaluation of performance, including precision, recall, and computational efficiency. The results demonstrate the efficiency and usability of this system in a real installation and promise to revolutionize automatic vehicle identification. Finally, the integration of artificial intelligence and machine learning technology into real-time license plate recognition signifies changes in traffic management, assessment safety and smart city plans. Therefore, interdisciplinary collaboration and continuous innovation are crucial to shaping a sustainable and balanced future for intelligent transportation systems.

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Next-Generation License Plate Detection and Recognition System Using YOLOv8

Cited by 3 publications

References 16 publications

A License Plate Recognition Method Based on YOLOv8-Pose and E-LPRNet

A License Plate Recognition Method Based on YOLOv8-Pose and E-LPRNet

Cost-Sensitive Road Obstacle Detection for Low-Cost Autonomous Vehicles

Real-time number plate detection using AI and ML

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