https://github.com/zaibten/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.
https://github.com/zaibten/helmet-detection-using-machine-learning-deep-learning
dataset flutter googlecolab jupyter-notebook machine-learning mobile-app model-training opencv python realtime-detection tensorflow yolov8
Last synced: 9 days ago
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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.
- Host: GitHub
- URL: https://github.com/zaibten/helmet-detection-using-machine-learning-deep-learning
- Owner: Zaibten
- Created: 2023-12-03T12:00:35.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-12-18T15:10:24.000Z (about 1 year ago)
- Last Synced: 2025-04-07T08:28:30.868Z (9 months ago)
- Topics: dataset, flutter, googlecolab, jupyter-notebook, machine-learning, mobile-app, model-training, opencv, python, realtime-detection, tensorflow, yolov8
- Language: Python
- Homepage:
- Size: 83.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚦 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.
3. 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 🖼️✨
