AI-Driven Safety: Automated Helmet Detection and License Plate Recognition

This research study addresses the increasing need for automated traffic enforcement solutions to improve road safety, focusing on helmet detection and license plate recognition for motorcyclists. With manual enforcement limited by factors such as human error, fatigue, and the sheer volume of traffic, an AI-driven system offers a scalable alternative. Leveraging YOLOv8 for object detection and EasyOCR for text extraction, the proposed system automates the detection of helmet violations and vehicle identification through number plates. The model operates by first detecting helmet usage, and if absent, proceeds to recognize the vehicle's number plate. It performs reliably in various challenging conditions like poor lighting, occlusion, and motion blur, making it suitable for real-time traffic monitoring. The research also addresses ethical considerations around privacy and data protection, ensuring compliance with legal standards. By reducing manual intervention, this solution enhances traffic enforcement efficiency, promotes road safety, and provides a scalable approach to monitoring traffic violations.