Advanced AI-Based License Plate Recognition and Pollution Com-pliance Monitoring for Motorbikes Using YOLOv8 and LLaMA OCR
Year: 2025
Type: article
Abstract: Motorbike traffic has significantly increased in urban areas, posing challenges for traffic management and environmental compliance. Existing License Plate Recognition (LPR) systems struggle with rec- ognizing non-standard motorbike plates, especially under adverse conditions such as low-light envi- ronments, occlusions, and distorted fonts. Additionally, real-time enforcement of pollution compliance remains underexplored. This study presents an advanced AI-based approach integrating YOLOv8 for high-precision license plate detection and LLaMA OCR, a transformer-based model, for robust char- acter recognition in challenging conditions. Furthermore, a Real-Time Compliance Monitoring Mod- ule (RT-CMM) is introduced to verify vehicle registration and pollution compliance through government databases. Experimental results on diverse datasets, including Indian motorbike plates, demonstrate an impressive 99.7% detection accuracy and 97.8% compliance verification success rate. The pro- posed system outperforms conventional methods and provides a scalable solution for smart traffic mon- itoring, regulation enforcement, and environmental protection in urban settings
Source: Journal of Neonatal Surgery
Authors: Akula Jayanth Babu, Nikhat Parveen
Institution: Koneru Lakshmaiah Education Foundation
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409891452