Real-Time License Plate Recognition Using YOLOv10 and OCR Integration for Autonomous Traffic Monitoring and Surveillance Systems

Abstract

The proposed method applies YOLOv10 deep learning to detect license plates with subsequent text extraction from plates through EasyOCR and OCR engines. This system architecture operates in real-time allowing its deployment in unmanned traffic surveillance systems law enforcement detection and automated parking operations. The YOLOv10 model achieves quick and reliable license plate detection through CSPNet and SCDD while working in dynamic settings. The system combines various OCR methods into its architecture for processing text with different font types and challenging visual factors. The model exhibits strong precision-recall abilities and generalization power based on Precision-Recall Curve and F1-Score and Confusion Matrix results which lead to a 0.986 mean Average Precision (mAP). The system functions independently and accepts real-time detection through either static images, moving video, or live source vide. This solution expands to accommodate usage in extensive traffic management systems which enable practical vehicle tracking alongside toll collection and security surveillance operations.