Helmet Detection Using Machine Learning & Deep Learning

This project focuses on a real-time Helmet Detection System to ensure road safety by identifying riders who are not wearing helmets. Integrated with a Flutter application, it provides an automated system for issuing challans (e-tickets) and capturing images of the rider and their number plate for record-keeping.

Key Features:
Helmet Detection:

Uses state-of-the-art Machine Learning and Deep Learning models to detect if a rider is wearing a helmet.
Real-time analysis through video feeds or captured images.
Rider Identification:

Automatically captures the rider's image upon helmet detection failure.
High accuracy ensures clear identification of violators.
License Plate Recognition:

Captures and recognizes the rider's vehicle number plate using OCR (Optical Character Recognition).
Helps in linking violations to registered owners.
Flutter Application Integration:

A user-friendly Flutter app allows authorities to manage violations, issue challans, and review captured data.
Displays violator details along with images for transparency.
Challan Generation:

Automatically generates e-challans for helmet violations.
Includes violator details, vehicle number, and images of the incident.
Tech Stack:
Deep Learning: Convolutional Neural Networks (CNNs) for image classification.
Machine Learning: Algorithms for license plate recognition.
Flutter: Cross-platform application development.
Backend: Cloud storage for storing images and data logs.
Benefits:
Promotes road safety and reduces accidents.
Streamlined violation management for traffic authorities.
Provides an efficient, automated solution to handle traffic rule enforcement.
This innovative system is a step forward in leveraging technology to ensure compliance with safety regulations while simplifying the workflow for traffic authorities.