Retro Scan AI

🚦 Project Overview

Retro Scan AI is a production-ready platform for automated road asset detection, scoring, and analytics. It leverages state-of-the-art YOLO models and a modern web dashboard to deliver actionable insights for infrastructure maintenance and compliance.

Key Benefits:

• Automated detection of road signs, markings, and studs
• Real-time scoring and compliance analytics
• Intuitive dashboard for visualization and reporting
• Robust fallback and error handling for seamless user experience

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🏗️ Architecture

graph TD
    A[User uploads image via UI] --> B[React Frontend]
    B --> C[API Call: /detect]
    C --> D[FastAPI Backend]
    D --> E[YOLO Model Inference]
    E --> F[Detection Results]
    F --> D
    D --> G[JSON Response]
    G --> B
    B --> H[Dashboard Visualization]
    B --> I[Fallback: Error/Offline Handling]

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📁 Project Structure

Retro Scan AI/
├── backend/           # FastAPI backend and YOLO logic
│   ├── app.py         # Main FastAPI app
│   ├── requirements.txt
│   └── ...
├── src/               # React frontend source code
│   ├── api/
│   ├── components/
│   ├── pages/
│   └── ...
├── public/            # Static assets
├── package.json       # Frontend dependencies
└── ...

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🚀 Quickstart

Prerequisites

• Python 3.10+
• Node.js 18+
• (Recommended) Git

Backend Setup

cd backend
python -m venv .venv
# On Windows:
.venv\Scripts\activate
# On macOS/Linux:
source .venv/bin/activate
pip install -r requirements.txt
uvicorn app:app --reload --host 127.0.0.1 --port 8000

Frontend Setup

npm install
npm run dev
# Open http://localhost:5173 in your browser

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🧑‍💻 Usage

API Example

Detect objects in an image:

curl -X POST "http://127.0.0.1:8000/detect" -F "file=@test.jpg" -F "speed=80"

Health check:

curl http://127.0.0.1:8000/health

UI Example

1. Open the dashboard in your browser.
2. Upload a road asset image.
3. View detection results, compliance status, and analytics.

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🔌 API Endpoints

• GET /health — Health check
• POST /detect — Run YOLO detection on an uploaded image

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⚙️ Configuration

• YOLO Model: The backend uses Ultralytics YOLO. Default: yolov8n.pt.
• Custom Model: Set the YOLO_MODEL environment variable to use a different model.

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🛠️ Troubleshooting

• Backend not found? Ensure the FastAPI server is running at http://127.0.0.1:8000.
• Module errors? Double-check your Python environment and installed dependencies.
• YOLO errors? Make sure the model file exists and is compatible with your Ultralytics version.
• CORS issues? The backend enables CORS for all origins by default.

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🤝 Contributing

Contributions are welcome! To contribute:

1. Fork the repository
2. Create a new branch (git checkout -b feature/your-feature)
3. Commit your changes
4. Push to your fork and open a Pull Request

Please see CONTRIBUTING.md for guidelines.

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❓ FAQ

Q: Can I use a custom YOLO model?
A: Yes, set the YOLO_MODEL environment variable to your model path.

Q: How do I deploy this in production?
A: Use a production server (e.g., Gunicorn with Uvicorn workers) and serve the frontend with a static file server or CDN.

Q: Where do I report bugs?
A: Please open an issue on GitHub.

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📄 License

This project is licensed under the MIT License.

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👤 Authors & Maintainers

R LAKSHMI PRANAY TEJ

For questions or support, please open an issue or contact the maintainer.