Intelligent Road Safety System: Integrating Machine Learning for Enhanced Speed Regulation and License Plate Verification

Road safety remains a critical issue globally, with speeding identified as a leading cause of traffic accidents. Traditional speed monitoring systems often fail to capture violations occurring between checkpoints. This study introduces an Intelligent Road Safety System that integrates an Average Speed Camera (ASC) with License Plate Recognition (LPR) technology. The proposed system uses YOLOvll to detect and recognize license plates with high accuracy in real-time. The system calculates average speeds by time stamping vehicle movements at predefined checkpoints. The average speed is then compared with the speed limit set for the specific road to identify over speeding. We also created a master database with the plate number and registration details to validate the vehicle's registration status. The proposed system is tested under different conditions such as low light, glare, and obstructions. Experimental results demonstrate a detection accuracy of mAP@50: 99.3% and recognition accuracy of mAP@50-95: 99.4%, with inference time less than 40ms. This study contributes to minimize speeding-related accidents, supporting Oman Vision 2040's goals of safer roads and resilient infrastructure.