Advanced AI-Based License Plate Recognition and Pollution Com-pliance Monitoring for Motorbikes Using YOLOv8 and LLaMA OCR
DOI:
https://doi.org/10.63682/jns.v14i15S.4052Keywords:
License Plate Recognition (LPR), YOLOv8, LLaMA OCR, Motorbike Monitoring, Pollu- tion Compliance, Traffic Management, Deep LearningAbstract
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
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