Towards the Safer Roads: Driver Helmet and Number Plate Detection System using YOLOv8 and Google Vision API

Now a days road safety is a growing concern, due to numerous accidents caused by non-compliance with road safety laws. This research represents an automated system for identification of driver helmet and number plate using YOLOv8 deep learning computer vision model combined with Google Vision API for text recognition from number plate leveraging latest cloud technology for enhanced accuracy and faster processing. The system is designed to efficiently detect whether a rider is wearing a helmet or not and simultaneously extract license plate information for identification purposes. Our method utilizes YOLOv8’s real time object detection features to reliably identify riders and their helmets in different lightning and environmental situations. After detecting a vehicle, we extract the number plate and process it with Google Vision API, which transforms the text from images into a format that can be processed. This two-tiered system allows for effective enforcement of traffic regulations and can be incorporated into smart surveillance systems to improve compliance with road safety. Experimental results show a strong ability to detect helmets and extract number plate information, indicating that this method could be a valuable tool for automated traffic monitoring. Future improvements might include facial recognition and vehicle tracking to create a more effective enforcement system. This research plays a significant role in advancing intelligent transportation systems by offering an efficient, AIpowered approach to enhancing road safety and law enforcement.