ppe-helmet-detection-yolov8

Gear Detection - PPE Detection using YOLO

This project detects Personal Protective Equipment (PPE) such as helmets, vests, and gloves in videos using the YOLO (You Only Look Once) model. It leverages ultralytics for YOLO inference and OpenCV for video processing.

📌 Features

• Real-time PPE Detection using a trained YOLO model.
• Bounding Boxes & Labels for detected safety gear.
• Confidence Scores to indicate detection accuracy.
• Scalable Display with adjustable frame resizing.

🛠️ Requirements

Before running the project, ensure you have the following dependencies installed:

📥 Installation

Follow these steps to set up and run the project:

pip install ultralytics opencv-python numpy

git clone https://github.com/your-username/GearDetection.git
cd GearDetection
pip install ultralytics opencv-python numpy

📁 Project Structure

The project is structured as follows:

GearDetection/
├── best.pt                 # Pre-trained YOLO model (replace with actual model file)
├── worker.mp4              # Sample video for detection
├── detect.py               # Main script for detection
├── README.md               # Project documentation

🚀 How to Use

Run the following command to start detection:

python detect.py

🖥️ Code Overview

• Loads a pre-trained YOLO model.
• Reads frames from video input.
• Performs detection and draws bounding boxes.
• Displays real-time PPE detection.

🎥 Example Output

Upon running the script, the model will detect PPE and display bounding boxes over detected objects:

Without Helmet:

With Helmet:

🔍 Notes

• Ensure best.pt is correctly placed inside the project folder.
• Modify scale = 0.3 in detect.py to adjust the display size.
• Replace worker.mp4 with your own video for testing.

❓ Troubleshooting

Issue: ModuleNotFoundError: No module named 'ultralytics'

pip install ultralytics

🤝 Contributing

Contributions are welcome! If you’d like to contribute:

1. Fork the repository.
2. Create a new branch.
3. Commit your changes.
4. Submit a pull request.

📜 License

This project is open-source and available under the MIT License.