Bar Detection and Conveyor Movement Monitoring

This project uses YOLOv8 to detect metal bars on a conveyor belt and monitor if the conveyor has stopped moving. It provides visual alerts when the conveyor is halted.

Features

• Real-time metal bar detection using YOLOv8
• Conveyor belt movement monitoring
• Visual alerts for conveyor stoppages
• Works with both video files and live camera feeds

Project Structure

├── model/
│   └── best.pt        # YOLOv8 trained model
├── assets/
│   └── conv.mp4       # Sample video for testing
├── main.py            # Main application file
├── requirements.txt   # Python dependencies
├── .gitignore         # Git ignore file
└── README.md          # Project documentation

Requirements

• Python 3.8+
• OpenCV
• Ultralytics YOLOv8
• NumPy

Setup

Virtual Environment Setup

1. Create a virtual environment:

# Windows
python -m venv venv

# Linux/macOS
python3 -m venv venv

2. Activate the virtual environment:

# Windows
venv\Scripts\activate

# Linux/macOS
source venv/bin/activate

3. Install the required packages:

pip install -r requirements.txt

Usage

1. Run the application:

python main.py

2. Configuration Options:

The following settings can be modified in main.py:

Parameter	Description
model_path	Path to your YOLOv8 model
video_path	Path to video file or camera index
output_path	Path to save the output video
stop_threshold	Number of frames to determine if conveyor is stopped
movement_tolerance	Pixel tolerance for movement detection

3. For live camera detection:
   • Uncomment the line # video_path = 0 in the configuration section

How It Works

1. Object Detection: The system uses a pre-trained YOLOv8 model to detect metal bars in each frame.

2. Movement Tracking: For each detected bar, the system tracks its centroid position across frames.

3. Stop Detection: The system calculates the average movement of all tracked objects between consecutive frames:

   • If movement falls below the movement_tolerance threshold, a counter is incremented
   • When the counter exceeds the stop_threshold, the conveyor is considered stopped

4. Visual Alerts:

   • Detected bars are highlighted with yellow rectangles
   • "OK" (green) indicates the conveyor is running normally
   • "Conveyor Halted" (red) warning appears when the system detects a stoppage