Helmet and Number Plate Detection Using Deep learning

Ensuring road safety has become increasingly important due to the rapid growth of two-wheeler vehicles and the rising number of traffic-related accidents.Non-compliance with helmet regulations significantly contributes to fatalities, while manual monitoring of traffic violations remains inefficient and error-prone.This paper proposes a robust deep learning-based system for simultaneous detection of helmet usage and vehicle number plates.The system employs advanced convolutional neural networks (CNNs) and object detection frameworks to accurately identify helmet-wearing riders and extract vehicle number plate information in real-time.A carefully curated dataset, comprising diverse traffic scenarios, is used to train and validate the model, ensuring high accuracy under varying lighting, occlusion, and environmental conditions.Experimental results demonstrate that the proposed system achieves superior detection performance compared to conventional methods, enabling automated enforcement of traffic regulations.The integration of helmet detection with number plate recognition provides a scalable solution for smart city surveillance, contributing to enhanced road safety, efficient law enforcement, and reliable accident monitoring.