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.
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.
- 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.
- REST API Backend: A robust and scalable backend system that caters to data storage, processing, and retrieval.
- PWA Client: An interactive web interface ensuring seamless user experience across devices.
- IoT Integration: Using drones equipped with Raspberry Pi 3 for real-time road surface scanning and image capturing.
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Prerequisites:
- Python 3.8+
- Node.js
- PostgreSQL server
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Installation:
git clone https://github.com/StepanTita/rdrone-front.git cd rdrone-front yarn install
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Usage:
yarn serve
We value all forms of contribution! If you find a bug ๐ or have a feature request ๐ฆ, please open an issue. If you'd like to contribute code, please fork the repository and create a pull request.
This project is licensed under the MIT License. See LICENSE for details.
yarn install
yarn serve
yarn build