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Effective Road Quality Mapping and Navigation
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Table of Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. Roadmap
  5. Contributing
  6. License
  7. Contact
  8. Acknowledgments

About The Project

Project Image

The proposed system addresses the issue of potholes and poor road quality in developing countries by utilizing an end-to-end architecture that combines both computer vision and sensor-based models. The product includes connected applications like an analytics dashboard for government use and a navigation application for consumers. This approach is an innovative, scalable, reliable, and can be deployed with minimal changes to existing infrastructure.

Key Features include:

  • Specialized software for collecting motion sensors data from IoT devices and streaming to our cloud pipeline.
  • An innovative ensemble of YOLOS and YOLOv8 models, achieving a 97.34% [email protected] score for pothole detection.
  • Advanced sensor-based models for road quality detection, achieving accuracies of 98.5% and 95.4%.
  • An Analytics Dashboard to manage the data and insights for government officials.
  • A Navigation Application for consumers to avoid potholes and poor road quality.

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Built With

Typescript React-Native Expo Redux NativeWind

Python FastAPI Pandas Numpy HuggingFace Ultralytics OpenCV Scikit-Learn Folium Matplotlib

SQS S3 Lambda EC2 Api-Gateway Google-Routes-API

DynamoDb MongoDB

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System Design

ipd flow(1)

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