Helmet Detection system which was prepared for TIE coimbatore Hackathon

A helmet detection system is a computer vision application designed to identify the presence of helmets in images or video streams. Leveraging deep learning models such as YOLO (You Only Look Once) and OpenCV for image processing, the system analyzes visual data in real-time.

Helmet Detection System is basically building for the Safety purpose.So,we can digitally protection.

HelmetDetectionSystem

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A real-time Helmet Detection System using YOLOv5 and OpenCV. Detects riders with or without helmets from webcam feed, raises sound alerts for violations, and supports custom-trained models. Built with Python, PyTorch, and CV tools for safety and surveillance applications.

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A web application for helmet detection, using Vue for the front-end, django for the back-end, and YOLO5 for the detection model.

This project is an AI-based solution for detecting helmets from video streams or pre-recorded videos. The system leverages machine learning and computer vision to enhance road safety by identifying whether individuals are wearing helmets in real-time or from recorded footage.

This project is made with the help of Streamlit.

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This project automatically detects whether a motorcyclist is wearing a helmet and extracts the vehicle registration number in case of violation. The system uses YOLOv8 for real-time helmet detection and Optical Character Recognition (OCR) for license plate recognition.