🚦 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 🛡️:

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

Rider Identification 📸:

1. Automatically captures the rider's image upon helmet detection failure.
2. High accuracy ensures clear identification of violators.

License Plate Recognition 🔢:

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

Flutter Application Integration 📱:

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

Challan Generation 📝:

1. Automatically generates e-challans for helmet violations.
2. Includes violator details, vehicle number, and images of the incident.

🧠 Tech Stack:

1. Deep Learning: Convolutional Neural Networks (CNNs) for image classification.
2. Machine Learning: Algorithms for license plate recognition.
3. Flutter: Cross-platform application development.
4. Backend: Cloud storage for storing images and data logs.

🌟 Benefits:

1. Promotes road safety and reduces accidents.
2. Streamlined violation management for traffic authorities.
3. 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. 🚴‍♂️👷‍♂️

📸 Some Screenshots of the Project 🖼️✨