Intelligent Real-Time Helmet and License Plate Detection Device for Traffic Monitoring

Abstract

In response to growing safety concerns on urban roadways, this study provides an intelligent real-time system for detecting helmets and license plates that is especially tailored to improve traffic monitoring and enforcement. Using YOLOv8's improved capabilities in conjunction with Spatial Pyramid Pooling (SPP), the suggested system reliably recognizes motorcycle helmet use and detects car license plates with high precision and speed. The addition of SPP improves the system's feature extraction capabilities, allowing for robust detection over a wide range of environmental situations, including different lighting and weather scenarios. This approach, which focuses on real-time processing, enables traffic officials to efficiently enforce helmet compliance and identify offenders, contributing to a safer urban transportation ecology. Experimental findings show that the device has excellent detection accuracy and processing efficiency, making it appropriate for use in intelligent transportation systems (ITS). The outcomes of this study show that AI-powered enforcement tools have the potential to increase road safety and expedite traffic monitoring activities.