README.md

metadata

license: mit
title: Helmet_Detection_OCR_ANPR
sdk: gradio
emoji: 📚
colorFrom: red
colorTo: yellow
short_description: Helmet_Detection_OCR_ANPR

Combined ANPR and Helmet Detection System

A comprehensive traffic violation detection system that combines Automatic Number Plate Recognition (ANPR) and Helmet Detection using YOLOv8.

Features

• Real-time license plate detection and recognition
• Helmet detection for two-wheeler riders
• Modern Gradio interface with real-time processing
• Adjustable confidence threshold for detection
• Combined visual annotations from both models
• Queue support for multiple users
• Comprehensive error handling

Prerequisites

• Python 3.8 or higher
• CUDA-capable GPU (recommended for better performance)
• 8GB RAM minimum

Installation

1. Clone the repository:

git clone <repository-url>
cd <repository-name>

2. Create and activate a virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

Usage

1. Start the application:

python app.py

2. Open your web browser and navigate to:

http://localhost:7860

3. Upload an image or use the example images to test the system.

Model Files

The following model files are required:

• ANPR_IND/licence_plat.pt: License plate detection model
• ANPR_IND/licence_character.pt: Character recognition model
• Helmet-Detect-model/best.pt: Helmet detection model

API Endpoints

The application exposes the following endpoints:

• /api/predict: POST endpoint for image processing
• /api/health: GET endpoint for health check

Deployment

Local Deployment

python app.py

Docker Deployment

docker build -t traffic-detection .
docker run -p 7860:7860 traffic-detection

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request

License

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