YODAVI

YODAVI - Smart Object Detection and Analysis for Vision Intelligence using YOLOv11

🏆 Recognized as FINALIST in Smart India Hackathon 2024
Problem Statement provided by Indo-Tibetan Border Police (ITBP)
Conducted in Lucknow, Uttar Pradesh

🚀 Quick Start • 📖 Documentation • 🎯 Features • 🛠️ Installation • 📊 Screenshots

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🎯 Overview

YODAVI is a cutting-edge object detection platform that combines the power of YOLOv11 with a sleek, professional web interface. Recognized as a FINALIST in Smart India Hackathon 2024, this project was developed to address critical security and surveillance challenges presented by the Indo-Tibetan Border Police (ITBP).

Originally designed for advanced computer vision applications, YODAVI has evolved into a comprehensive solution for real-time object detection and analysis, specifically tailored for border security and surveillance operations.

🌟 Key Highlights

• 🔥 Real-time Detection: 30+ FPS performance with YOLOv11
• 💻 Professional UI: Modern web interface with responsive design
• 📊 Advanced Analytics: Live charts and detection statistics
• 🎨 Drag & Drop: Intuitive file upload with visual feedback
• 📱 Mobile Ready: Responsive design for all devices
• ⚡ Lightweight: Only 6.2MB model size with <500MB RAM usage

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📊 Screenshots

Dashboard Overview

Professional dashboard with real-time detection feed and analytics

Car Detection Results

Vehicle detection with high accuracy and confidence scores

Night Vision Detection

Advanced night vision object detection capabilities

Thermal Detection

Thermal imaging object detection for specialized applications

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🎯 Features

🖥️ Professional Web Interface

• Modern Design: Clean, intuitive interface with professional styling
• Real-time Feed: Live webcam detection with smooth 30+ FPS
• Interactive Controls: Start/stop detection, capture frames, upload images
• Status Indicators: Visual feedback for all operations
• Responsive Layout: Works seamlessly on desktop, tablet, and mobile

🚀 High-Performance Detection

• YOLOv11 Integration: Latest YOLO model for superior accuracy
• Optimized Processing: Smart frame sampling for optimal performance
• Multi-format Support: JPG, PNG, BMP image processing
• Real-time Analytics: Live FPS counter and session tracking

📊 Advanced Analytics

• Live Statistics: Real-time object counting and session metrics
• Interactive Charts: Pie charts showing object distribution
• Detection History: Comprehensive logging with SQLite database
• Export Capabilities: Download detection results and captured frames

🎨 User Experience

• Drag & Drop Upload: Intuitive file upload interface
• Toast Notifications: User-friendly feedback system
• Loading States: Visual indicators for all operations
• Keyboard Shortcuts: Quick access to common functions

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🛠️ Installation

Prerequisites

Before installing AI Vision Pro, ensure you have:

• Python 3.8+ installed on your system
• Webcam (built-in or USB) for live detection
• Modern web browser (Chrome, Firefox, Safari, Edge)
• 4GB RAM recommended for optimal performance

Step 1: Clone Repository

git clone https://github.com/Anand0295/YODAVI.git
cd YODAVI

Step 2: Create Virtual Environment (Recommended)

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
venv\Scripts\activate

Step 3: Install Dependencies

pip install -r requirements.txt

Dependencies installed:

• ultralytics - YOLOv11 model and inference
• opencv-python - Computer vision operations
• flask - Web framework
• flask-socketio - Real-time communication
• numpy - Numerical operations

Step 4: Download YOLO Model

The YOLOv11 model will be automatically downloaded on first run (6.2MB).

Step 5: Run Application

python web_app.py

You should see:

YODAVI - Object Detection Platform
=========================================
🚀 Starting server on http://localhost:3000
📊 Professional web interface ready

Step 6: Access Web Interface

Open your browser and navigate to:

http://localhost:3000

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🚀 Quick Start

1. Start Live Detection

1. Click "Start Detection" in the sidebar
2. Allow camera permissions when prompted
3. Watch real-time object detection in action

2. Upload Images

1. Drag and drop an image onto the upload area
2. Or click to browse and select files
3. View detection results instantly

3. View Detection Results

1. During live detection, click "Capture"
2. Frame is automatically downloaded with detections
3. Perfect for documentation and analysis

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📖 Documentation

Configuration

Camera Settings

The application automatically optimizes camera settings:

# Default camera configuration
FRAME_WIDTH = 640
FRAME_HEIGHT = 480
TARGET_FPS = 30

Detection Thresholds

Customize confidence thresholds for different object classes:

confidence_thresholds = {
    'person': 0.7,
    'car': 0.8,
    'knife': 0.5,
    'default': 0.6
}

Performance Optimization

• Frame Sampling: Processes every 4th frame for optimal performance
• Quality Settings: JPEG compression at 80% for balance of quality/speed
• Memory Management: Limited detection history (50 recent detections)

API Endpoints

Endpoint	Method	Description
/	GET	Main dashboard interface
/start_webcam	POST	Start live detection
/stop_webcam	POST	Stop live detection
/upload_file	POST	Upload and process image
/statistics	GET	Get detection statistics
/clear_logs	POST	Clear detection history

Database Schema

CREATE TABLE detections (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
    class_name TEXT,
    confidence REAL,
    source TEXT
);

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🎨 Customization

Styling

Modify static/css/photobooth.css to customize the interface:

/* Change primary color */
.btn-primary {
    background: #your-color;
}

/* Modify layout */
.main-container {
    max-width: your-width;
}

Detection Classes

Add custom object classes by training your own YOLO model:

# Replace model file
self.model = YOLO('your-custom-model.pt')

UI Components

Extend functionality by modifying static/js/photobooth.js:

// Add custom features
class AIVisionPro {
    // Your custom methods
}

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🔧 Troubleshooting

Common Issues

Camera Not Working

# Check camera permissions
# On macOS: System Preferences > Security & Privacy > Camera
# On Windows: Settings > Privacy > Camera

Port Already in Use

# Change port in web_app.py
socketio.run(app, port=3001)  # Use different port

Model Download Issues

# Manually download model
wget https://github.com/ultralytics/assets/releases/download/v0.0.0/yolo11n.pt

Performance Issues

• Reduce frame rate: Modify frame sampling interval
• Lower resolution: Adjust camera resolution settings
• Close other applications: Free up system resources

Debug Mode

Enable debug mode for development:

socketio.run(app, debug=True, port=3000)

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📊 Performance Benchmarks

System Requirements

Component	Minimum	Recommended
CPU	Dual-core 2.0GHz	Quad-core 2.5GHz+
RAM	2GB	4GB+
Storage	1GB	2GB+
Camera	480p	720p+

Performance Metrics

Metric	Value	Notes
Detection Speed	30+ FPS	On recommended hardware
Model Size	6.2MB	YOLOv11n optimized
Memory Usage	<500MB	Including web interface
Startup Time	<5 seconds	First run may take longer
Accuracy	90%+	On COCO dataset

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🤝 Contributing

We welcome contributions! Here's how to get started:

Development Setup

# Fork the repository
git fork https://github.com/your-username/ai-vision-pro

# Create feature branch
git checkout -b feature/amazing-feature

# Make changes and commit
git commit -m "Add amazing feature"

# Push to branch
git push origin feature/amazing-feature

# Create Pull Request

Contribution Guidelines

• Follow PEP 8 style guidelines
• Add tests for new features
• Update documentation
• Ensure cross-platform compatibility

Areas for Contribution

• Custom model training interface
• Multi-camera support
• Cloud deployment guides
• Mobile app development
• Performance optimizations

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📄 License

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

MIT License

Copyright (c) 2024 AI Vision Pro

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

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🙏 Acknowledgments

🏆 Smart India Hackathon 2024

• Ministry of Education, Government of India for organizing the prestigious hackathon
• Indo-Tibetan Border Police (ITBP) for providing the challenging problem statement
• Lucknow Host Institution for excellent event management and facilities
• Jury Panel for recognizing our innovation as a finalist solution

🛠️ Technical Contributors

• Ultralytics for the amazing YOLOv11 model
• OpenCV community for computer vision tools
• Flask team for the excellent web framework
• Contributors who help improve this project

🎯 Special Recognition

This project addresses real-world border security challenges and demonstrates the potential of AI in national security applications.

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YODAVI - Built with ❤️ for smart vision intelligence

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# YODAVI