Hybrid Euro Coin Detection System

A real-time Euro coin detection and classification system using a Hybrid Pipeline Architecture that combines YOLO11s deep learning with classical computer vision techniques for high accuracy and stability.

🎯 Overview

This system detects and classifies all 8 types of Euro coins (1c, 2c, 5c, 10c, 20c, 50c, 1€, 2€) in real-time using a custom-trained YOLO11s model combined with size refinement, color classification, and consensus voting.

✨ Key Features

• 🚀 Real-time Detection: Optimized for 20+ FPS with frame skipping
• 🎯 High Accuracy: Custom-trained YOLO11s model (99.1% mAP50)
• 🔬 Multi-method Classification: YOLO + Size + Color with consensus voting
• 📊 Temporal Smoothing: Reduces flickering with frame history tracking
• 🎨 Color Classification: Robust HSV-based color analysis
• 📏 Size-based Classification: Calibrated pixel-to-millimeter conversion
• 🖥️ Side-by-Side GUI: Camera feed + Statistics panel
• ⚙️ Calibration System: Coin-based calibration for accurate sizing

🏗️ Architecture

The system uses a Hybrid Pipeline that combines multiple detection methods:

Camera Frame
    ↓
YOLO11s Detection (Primary)
    ↓
Hybrid Pipeline Processing
    ├─► Size Refinement (Hough Circles - Optional)
    ├─► Color Classification (HSV Analysis)
    ├─► Size Classification (Calibrated)
    └─► Consensus Voting (Weighted Combination)
    ↓
Temporal Smoothing (Frame History)
    ↓
Display (Camera + GUI Panel)

Component Overview

• CoinDetectionApp (main.py): Main application coordinator
• CoinDetector (detector.py): YOLO11s model wrapper
• HybridPipeline (hybrid.py): Multi-method classification
• Calibrator (calibration.py): Pixel-to-mm calibration
• Utils (utils.py): Utility functions

For detailed architecture, see Report/Report.md.

📸 Screenshots

Screenshots will be added to assets/screenshots/

🚀 Quick Start

Installation

1. Clone the repository:

   git clone https://github.com/xampos101/Hybrid-Euro-Detection-System.git
   cd Hybrid-Euro-Detection-System

2. Install dependencies:

   pip install -r requirements.txt

3. Verify model file:

   • Ensure V2/model/best.pt exists (YOLO11s trained model)

Basic Usage

python new_pipeline/main.py

Calibration (Recommended)

For best accuracy, calibrate the system first:

1. Press '2' for 2€ coin calibration
2. Place ONLY the selected coin in frame
3. Wait for green circle
4. Press SPACE to confirm

📁 Project Structure

Hybrid-Euro-Detection-System/
├── new_pipeline/          # Main pipeline code
│   ├── main.py           # Application entry point
│   ├── detector.py       # YOLO detection wrapper
│   ├── hybrid.py         # Hybrid classification pipeline
│   ├── calibration.py   # Calibration system
│   ├── utils.py          # Utility functions
│   └── config.py         # Configuration settings
│
├── V2/                   # Models
│   └── model/
│       └── best.pt       # YOLO11s trained model
│
├── Report/               # Documentation
│   ├── Report.md        # Technical report
│   └── FlowDiagrams.md  # Mermaid flow diagrams
│
├── docs/                 # Additional documentation
│   └── training/        # Training results and plots
│
├── records/              # Demo videos
│   └── *.mp4           # Video demonstrations
│
└── assets/              # Images and screenshots
    ├── screenshots/     # Application screenshots
    └── diagrams/        # Exported diagrams

🎓 YOLO11s Model

Training Details

• Architecture: YOLO11s (small variant)
• Base Model: Pretrained yolo11s.pt
• Epochs: 20
• Dataset: Custom Euro coins dataset
• Final Performance:
  • Precision: 97.7%
  • Recall: 97.0%
  • mAP50: 99.1%
  • mAP50-95: 90.7%

Training Results

For detailed training information, see docs/training/training_results.md.

🎥 Demo Videos

Demo videos are available in the records/ folder:

• FINAL_RECORD.mp4 - Final demonstration
• Recording 2026-01-15 160914.mp4 - Latest recording
• Additional recordings available

See records/README.md for detailed descriptions.

📚 Documentation

• Report/Report.md: Comprehensive technical report

  • Architecture overview
  • Component descriptions
  • Algorithms and logic
  • Configuration guide

• Report/FlowDiagrams.md: Mermaid flow diagrams

  • Main application flow
  • Detection pipeline
  • Consensus voting
  • Temporal smoothing
  • Color classification

• docs/training/training_results.md: Training documentation

  • Training configuration
  • Results and metrics
  • Performance analysis

⚙️ Configuration

All settings are in new_pipeline/config.py:

• YOLO Settings: Confidence threshold, IOU threshold, image size
• Hybrid Pipeline: Hough refinement, color thresholds
• Temporal Smoothing: History size, matching thresholds
• Performance: Frame skipping, optimization settings

🎮 Controls

• 'q': Quit application
• 'd': Toggle debug mode
• '2': Calibrate with 2€ coin
• '1': Calibrate with 1€ coin
• '5': Calibrate with 50c coin
• SPACE: Confirm calibration
• ESC: Cancel calibration

🔧 Requirements

• Python 3.8+
• OpenCV (cv2)
• NumPy
• Ultralytics YOLO
• PyTorch (CPU or CUDA)

Install all dependencies:

pip install -r requirements.txt

🏆 Performance

• Detection Speed: 20+ FPS (with optimizations)
• Accuracy: 99.1% mAP50 (YOLO11s model)
• Classification: Multi-method consensus voting
• Stability: Temporal smoothing reduces flickering

🛠️ Technologies Used

• YOLO11s: Custom-trained deep learning model
• OpenCV: Image processing, Hough circles, camera capture
• NumPy: Array operations and mathematical calculations
• Ultralytics: YOLO framework
• PyTorch: Deep learning backend

📊 System Architecture

The system combines:

• Deep Learning (YOLO11s): Fast and accurate detection
• Classical CV (Hough, Color): Robust classification
• Temporal Smoothing: Stability across frames
• Consensus Voting: Weighted combination for accuracy

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📝 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

• Ultralytics for the YOLO framework
• OpenCV community for excellent computer vision tools
• Euro coin specifications from official sources

📧 Contact

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

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Built with ❤️ using YOLO11s and OpenCV