🎯 DartNet - AI-Powered Dart Scoring System

An experimental computer vision system for automatic dart scoring using deep learning

Features • Installation • Usage • Architecture • Contributing

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🚧 Project Status

⚠️ WORK IN PROGRESS: This project is under active development and is not production-ready. Expect breaking changes and incomplete features.

📖 About

DartNet is an innovative AI-powered system that automatically detects and scores dart throws using computer vision and deep learning. The project combines synthetic data generation, real-time video analysis, and various detection methods to create a comprehensive dart scoring solution.

🎯 Key Components

• 3D Synthetic Data Generator - Creates realistic training data using Blender
• YOLO-based Detection - Multiple trained models for dart tip detection
• Multi-method Impact Detection - Accelerometer, video delta, and pure video analysis
• Real-time Scoring GUI - Interactive interface with sound effects
• Hardware Integration - Support for GranBoard and custom sensors

✨ Features

Current Capabilities

• ✅ 3D Data Generation - Photorealistic synthetic dartboard and dart renders
• ✅ Multiple Detection Models - Various YOLO models for different scenarios
• ✅ Impact Detection - Multiple methods including accelerometer and video analysis
• ✅ GUI Scorer - Basic scoring interface with game logic
• ✅ Sound System - Audio feedback for hits and game events
• ✅ MQTT Integration - Real-time communication with sensors

🚧 Known Limitations

• ❌ Detection accuracy needs improvement
• ❌ Limited to specific board configurations
• ❌ Requires manual calibration for new environments
• ❌ Performance optimization needed
• ❌ Multi-player support incomplete

🛠️ Installation

Prerequisites

• Python 3.8 or higher
• OpenCV
• CUDA-capable GPU (recommended for real-time inference)
• Webcam or IP camera
• (Optional) Accelerometer sensor with MQTT support

Setup

1. Clone the repository

   git clone https://github.com/yourusername/DartNet.git
   cd DartNet

2. Create a virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Download pre-trained models (if available)

   # Models should be placed in the root directory
   # best_s_tip_boxes640_B.pt
   # best_temporal_A.pt
   # etc.

🚀 Usage

Quick Start

1. Generate Training Data (Optional)

   python generator/gen_dataset.py --num-images 1000

2. Run the Dart Scorer

   python scorer_gui.py

3. Live Detection Demo

   python live.py

Advanced Usage

Using Different Detection Methods

# Accelerometer + Video
python dart_impact_detector.py

# Video-only detection
python live.py --detector video-only

Training Custom Models

python train.py --dataset ./dataset --epochs 100

🏗️ Architecture

System Overview

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│  Video Capture  │────▶│ Impact Detection │────▶│  Dart Scoring   │
│   (Webcam/IP)   │     │  (Multi-method)  │     │     Engine      │
└─────────────────┘     └──────────────────┘     └─────────────────┘
         │                       │                         │
         ▼                       ▼                         ▼
┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│ YOLO Detection  │     │ MQTT Sensors     │     │   GUI Display   │
│    Models       │     │ (Accelerometer)  │     │  Sound Effects  │
└─────────────────┘     └──────────────────┘     └─────────────────┘

Key Modules

• dart_impact_detector.py - Multi-method impact detection system
• target_detector.py - YOLO-based dart tip detection
• scorer_gui.py - Game scoring logic and GUI
• generator/ - 3D synthetic data generation pipeline
• tools.py - Utility functions for image processing

📸 Screenshots

View Screenshots

Detection in Action

Coming soon...

Scorer GUI

Coming soon...

3D Generator Output

🗺️ Roadmap

Phase 1: Core Functionality (Current)

• Basic detection pipeline
• Simple scoring GUI
• Improve detection accuracy to 95%+
• Add calibration wizard

Phase 2: Enhanced Features

• Multi-player support
• Game variants (Cricket, Around the Clock)
• Statistics tracking
• Online leaderboards

Phase 3: Advanced Integration

• Mobile app companion
• Voice announcements
• Tournament mode
• AR visualization

🤝 Contributing

We welcome contributions! This project is in early development, so there are many opportunities to help.

How to Contribute

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Areas Needing Help

• 🎯 Improving detection accuracy
• 📊 Adding more game modes
• 🎨 UI/UX improvements
• 📝 Documentation
• 🧪 Testing and bug fixes

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• YOLOv8 by Ultralytics for object detection
• OpenCV community for computer vision tools
• Blender for 3D rendering capabilities
• All contributors and testers

📞 Contact

For questions or suggestions, please open an issue on GitHub.

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Made with ❤️ for the darts community