Enhancing Road Safety Through AI-Powered Helmet Detection and Connected Vehicle Systems

Enhancing Road Safety Through AI-Powered Helmet Detection and Connected Vehicle Systems

Two-wheeler riders without helmets account for a disproportionate share of fatal road accidents — and manual traffic enforcement cannot scale to monitor every road, every hour. This project delivers an intelligent road safety monitoring platform that combines real-time computer vision with connected vehicle technology. The AI detection module uses deep learning object detection models (YOLOv8) fine-tuned to accurately identify motorcycles and classify riders as helmeted or non-helmeted — even in challenging conditions including motion blur, occlusion, night scenes, and rain. The system processes live traffic camera feeds or uploaded footage, automatically logging violations with timestamped image evidence, vehicle number extraction via ANPR (Automatic Number Plate Recognition), and location tagging. The connected vehicle layer extends safety beyond enforcement: IoT-enabled vehicles and ride systems receive real-time alerts, and fleet operators can monitor helmet compliance across their riders. An analytics dashboard visualizes violation trends by location, time, and zone, supporting data-driven traffic planning and automated e-challan workflows. Built with Python, YOLO, OpenCV, and a web dashboard, this is a high-impact computer vision project for smart-city developers.

Components

Python 3.8+
YOLOv8
OpenCV
ANPR module
Flask web dashboard
Traffic camera / video feeds

Key Features

• YOLOv8 detection of motorcycles with helmeted vs non-helmeted rider classification
• Robust performance under motion blur, occlusion, night scenes and rain
• ANPR vehicle number extraction with timestamped violation evidence
• Connected vehicle alerts and fleet-wide helmet compliance monitoring
• Violation trend analytics by location, time and zone for e-challan workflows

Applications

Traffic police, smart city projects, fleet operators and computer vision students building AI traffic management systems.