AI-Powered Helmet Compliance Detection System

Overview

The AI-Powered Helmet Compliance Detection System is designed to enhance safety measures by ensuring that individuals are wearing helmets. Utilizing the YOLOv5 model for object detection, this system can accurately identify whether a person is wearing a helmet or not. The project is deployed using Flask, providing a simple web interface for real-time detection.

Features

• Real-time helmet detection using YOLOv5
• Easy-to-use web interface built with Flask
• High accuracy and fast processing
• Scalable and flexible architecture

Tech Stack

• Machine Learning: YOLOv5
• Backend: Flask
• Frontend: HTML, CSS, JavaScript
• Deployment: Docker (optional)

Installation

Prerequisites

• Python 3.7+
• Flask
• PyTorch
• OpenCV
• YOLOv5

Steps

1. Clone the Repository

   git clone https://github.com/your_username/helmet-compliance-detection.git
   cd helmet-compliance-detection

2. Install Dependencies

   pip install -r requirements.txt

3. Download YOLOv5 Weights Download the YOLOv5 weights from the official YOLOv5 repository or directly via:

   wget https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt -O yolov5s.pt

4. Run the Flask App

   export FLASK_APP=app.py
   flask run

   Access the app at http://127.0.0.1:5000

Screenshots

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

License

This project is licensed under the MIT License.

Acknowledgments

• YOLOv5 by Ultralytics
• Flask Web Framework
• OpenCV

Contact

For any inquiries or support, please contact.