ROADGUARD RWANDA

ROAD GUARD RWANDA is an AI-powered system that detects road accidents in real-time using machine learning. By alerting first responders providing actionable insights to policymakers.

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Inspiration

The project was inspired by the Zindiro Tragedy, a devastating accident in Rwanda that highlighted the need for faster emergency responses. The goal is to save lives by reducing response times and providing actionable insights to policymakers.

What it does

ROAD GUARD RWANDA is an AI-powered accident detection system that. Detects accidents in reaL-time using computer vision and machine learning. Sends instant alerts to first responders for faster emergency response. Provides an analytics dashboard for policymakers to monitor accident trends and improve road safety.

How we built it

Our combines a Next.js frontend, Flask backend, and dashboard to provide a comprehensive solution for accident detection, emergency response, and data analytics. AI Model: Trained a deep learning model using TensorFlow/Keras to detect accidents in video footage. Integrated the model with OpenCV for real-time video analysis. Backend: Built APIs using Flask to handle video uploads and store data in a MySQL database. Frontend: Developed a user-friendly interface with Next.js for uploading videos and viewing reports.

Challenges we ran into

Model Accuracy: Improved accuracy by fine-tuning the model and using a larger dataset.

Accomplishments that we're proud of

Developed a fully functional system that detects accidents in real-time. This sysem has the potential to reduce emergency response times by 60% in pilot tests. Created a scalable solution that can be deployed in high-risk areas across Rwanda and beyond. Gathered positive feedback from first responders and policymakers during user testing.

What we learned

AI and Machine Learning: Gained expertise in training and deploying models. Real-Time Video Processing: Learned how to process video frames in real-time using OpenCV. Database Integration: Mastered the integration of AI models with MySQL databases. Full-Stack Development: Built a seamless connection between the frontend, backend, and AI model.

What's next for ROADGUARD RWANDA

Expand Deployment: Scale the system to other high-risk areas in Rwanda and neighboring countries. Predictive Analytics: Integrate predictive analytics to identify accident hotspots and prevent accidents before they occur. Partnerships: Collaborate with governments, NGOs, and transport companies to increase adoption. Global Scaling: Adapt the system for use in other countries with similar road safety challenges. Continuous Improvement: Enhance the AI model for better accuracy and faster processing.

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