Convolutionary Neural Networks Road Quality Control System 🚗 🛣️

Demo

RDrONE.demo.mp4

This project presents a sophisticated software system built with the goal of ensuring and monitoring the quality of road surfaces in cities. Leveraging cutting-edge technologies such as Convolutionary Neural Networks and Image Recognition, this system aims to serve as an indispensable tool for contractors, drivers, and pedestrians alike.

🎯 Purpose

The development of this system seeks to:

• Offer contractors a convenient monitoring mechanism for road quality.
• Alert drivers and pedestrians about potential road surface problems in real-time.
• Enhance the overall road safety standards in the city.

🔧 Technology Stack

• Machine Learning & Image Processing: Python, TensorFlow, Transfer Learning.
• Backend: Django (compliant with OpenAPI specification Version 3.0.3), PostgreSQL.
• Frontend: JavaScript, HTML, CSS combined with Vue.js and Progressive Web Application (PWA) capabilities.
• Hardware Integration: Raspberry Pi 3 and Drones.

🛠️ System Components

1. REST API Backend: A robust and scalable backend system that caters to data storage, processing, and retrieval.
2. PWA Client: An interactive web interface ensuring seamless user experience across devices.
3. IoT Integration: Using drones equipped with Raspberry Pi 3 for real-time road surface scanning and image capturing.

🚀 Getting Started

1. Prerequisites:

   • Python 3.8+
   • Node.js
   • PostgreSQL server

2. Installation:

   git clone https://github.com/StepanTita/rdrone-back.git
   cd rdrone-back
   pip install -r requirements.txt

3. Usage:

   python manage.py runserver

🤝 Contributing

We value all forms of contribution! If you find a bug 🐞 or have a feature request 📦, please open an issue. If you'd like to contribute code, please fork the repository and create a pull request.

📃 License

This project is licensed under the MIT License. See LICENSE for details.