🛡️ P8GP-G01: Advanced Object Detection System

A real-time safety monitoring system powered by AI and deep learning

📄 Project Files & Documentation

📚 Essential Files Overview

File	Purpose	Description
📄 LICENSE	Legal Protection	Non-commercial use license preventing unauthorized commercial usage
📄 .gitignore	Version Control	Comprehensive exclusion rules for Python, AI models, and temporary files
📄 README.md	Documentation	Complete project guide, setup instructions, and feature overview
📁 Project_Picture/	Visual Assets	Application screenshots and interface previews for documentation
📁 UI/	Main Application	Core application code, models, and resources

🔒 License Information

This project is protected under a Non-Commercial License which:

• ✅ Permits: Personal use, educational purposes, research, and open-source contributions
• ❌ Prohibits: Commercial use, business applications, revenue generation, and corporate deployment
• 📋 Requires: Attribution to original authors and license inclusion in distributions

🚫 Git Exclusions (.gitignore)

The comprehensive .gitignore file excludes:

• Python cache files and virtual environments
• AI model files (*.pt, *.pth) to prevent large file commits
• Generated detection images and temporary files
• IDE-specific files and OS-generated files
• Sensitive configuration and log files

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🎨 Visual Gallery & Screenshots

📱 Application Interface Preview

The P8GP-G01 system features a modern, intuitive interface designed for seamless safety monitoring:

Screenshot showcasing the main application interface with real-time detection capabilities

🔍 Key Interface Elements Highlighted:

• Live Video Feed: Real-time camera stream with detection overlays
• Detection Status Panel: Visual indicators for safety equipment monitoring
• Control Dashboard: Comprehensive monitoring and statistics display
• Interactive Elements: User-friendly buttons and navigation controls

📸 More Screenshots: Additional project images and interface previews are available in the Project_Picture/ directory

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📸 Project Showcase

🎯 Main Application Interface

Real-time object detection in action - showcasing helmet, mask, and smoking detection capabilities

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📖 Project Overview

P8GP-G01 is a cutting-edge real-time object detection system engineered for safety monitoring applications. Leveraging state-of-the-art YOLO models (YOLOv5n and custom-trained variants), the system provides intelligent detection capabilities for critical safety equipment and hazardous situations including Helmets, Smoking, Fire, Smoke, and Face Masks.

The system features an intuitive PyQt6-powered graphical user interface that delivers seamless user experience with real-time monitoring, automated image saving, and comprehensive detection analytics. Built for cross-platform compatibility, it supports multiple camera sources and includes an elegant animated welcome interface.

✨ Key Features

🔍 Real-Time Detection Advanced safety equipment recognition Multi-class object detection (Helmets, Masks, Smoking, Fire, Smoke) High-accuracy YOLO model implementation Real-time confidence scoring 🖥️ Intelligent GUI Modern welcome screen with animations Live video feed with detection overlays Comprehensive control dashboard Interactive image gallery system

📹 Camera Management Universal camera detection (Windows/Linux/macOS) Multiple video source support Automatic device enumeration Platform-optimized camera handling 💾 Smart Data Management Automated image categorization and saving Organized folder structure by detection type Gallery preview with thumbnail generation Detection statistics and analytics

🎯 Monitoring Capabilities

Detection Type	Description	Use Case
🦺 Safety Helmets	Hard hat and protective headgear detection	Construction sites, industrial facilities
😷 Face Masks	Medical and protective mask identification	Healthcare, public safety compliance
🚬 Smoking Detection	Cigarette and smoking activity recognition	No-smoking zone enforcement
🔥 Fire Detection	Flame and fire hazard identification	Emergency response, safety monitoring
💨 Smoke Detection	Smoke plume and vapor detection	Early warning systems

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🛠️ Prerequisites & System Requirements

System Requirements

Component	Minimum Requirement	Recommended
Operating System	Windows 10, Linux (Ubuntu 18.04+), macOS 10.15+	Latest stable versions
Python Version	Python 3.10+	Python 3.11+
RAM	4 GB	8 GB+
Storage	2 GB free space	5 GB+
Camera	USB 2.0 webcam	USB 3.0 HD webcam
CPU	Dual-core 2.0 GHz	Quad-core 2.5 GHz+

Required Software

• Python 3.10+ (Tested extensively with Python 3.10; compatible with newer versions including 3.12.4)
• pip (Python package manager)
• Compatible webcam or video capture device
• (Optional) Raspberry Pi for embedded deployment

📚 Dependencies

All dependencies are precisely defined in UI/requirements.txt with tested version compatibility:

Core Libraries

torch==2.3.1              # PyTorch deep learning framework
opencv-python==4.9.0.80   # Computer vision and image processing
numpy==1.26.4             # Numerical computing foundation
ultralytics==8.1.0        # YOLOv5/YOLOv8 implementation
PyQt6==6.6.1              # Modern GUI framework (stable as of March 2025)
pygrabber==0.2            # Enhanced Windows camera detection (DirectShow)

Built-in Python Modules

These are included with Python standard library - no installation required:

• platform - System platform detection
• os - Operating system interface
• subprocess - Process management
• sys - System-specific parameters
• time - Time-related functions

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📦 Installation & Setup Guide

Step 1: Repository Setup

# Clone the repository (if applicable)
git clone <repository-url>
cd P8GP_G01

Step 2: Virtual Environment (Recommended)

# Create isolated environment
python -m venv venv

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

# Verify activation (prompt should show (venv))

Step 3: Navigate to Project Directory

cd UI

Step 4: Install Dependencies

# Install all required packages
pip install -r requirements.txt

# Verify installation
pip list

Step 5: Model Verification

Ensure all pre-trained models are present in UI/Models/:

Model File	Purpose	Status
Fire_Detection.pt	Fire and flame detection	✅ Required
Helmet_Detection.pt	Safety helmet identification	✅ Required
Mask_Detection.pt	Face mask detection	✅ Required
Smoking_Detection.pt	Smoking activity detection	⚠️ Optional (testing)

Note: If model files are missing, contact the development team or download from the official model repository.

Step 6: Directory Structure Verification

Confirm the following structure exists within your project:

P8GP_G01/
├── 📄 LICENSE                        # Non-commercial license file
├── 📄 .gitignore                     # Git exclusion rules
├── 📄 README.md                      # Main documentation
├── 📁 Project_Picture/               # Project screenshots
│   └── amin1.png                    # Application interface screenshot
└── 📁 UI/                           # Main application directory
    ├── 📁 Codes/                    # Python source code
    ├── 📁 Imgs/                     # Application assets
    │   ├── P1.png                  # Welcome background
    │   └── icon.png                # App icon
    ├── 📁 Images/                   # Detection output storage
    │   ├── 📁 masks/               # Mask detection saves
    │   ├── 📁 Helmets/             # Helmet detection saves
    │   ├── 📁 smoke/               # Smoke detection saves
    │   ├── 📁 Fire/                # Fire detection saves
    │   ├── 📁 Cigarettes/          # Cigarette detection saves
    │   └── 📁 smoking/             # Smoking activity saves
    ├── 📁 Models/                   # AI model files
    ├── requirements.txt             # Dependencies
    └── README.md                    # UI-specific documentation

📋 Important: Ensure the Project_Picture/ folder contains your application screenshots for proper documentation display.

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🚀 Usage Instructions

Launch Application

# Ensure you're in the UI directory
cd UI

# Start the application
python Codes/Application.py

1. Welcome Screen Interaction

• Startup: Application begins with an animated welcome screen displaying "P8GP-G01"
• Navigation: Click "Get Started" to proceed to the main detection interface
• Animation: Enjoy the smooth transition effects and professional branding

2. Main Interface Navigation

📺 Video Display Panel

• Live Feed: Real-time camera stream with AI-powered object detection
• Detection Overlays: Colored bounding boxes around detected objects
• Confidence Scores: Percentage accuracy displayed for each detection
• Multi-object Support: Simultaneous detection of multiple object types

🎛️ Control Panel Features

• Real-time Status: Live detection monitoring with visual indicators
• Object Counters: Dynamic count of detected objects by category
• Statistics Table: Comprehensive "Have" vs "Don't Have" metrics
• Future Plans: Expandable table for additional monitoring features

📷 Camera Management

• Active Cameras: Click to browse and select from detected camera devices
• Multi-platform Detection: Automatic recognition across different operating systems
• Hot-swapping: Switch between cameras without restarting the application

🖼️ Image Management System

• Save Toggle: Enable/disable "Save Detected Images" functionality
• Gallery Access: Browse categorized galleries (Masks, Safety Hats, Smoking, Fire, Smoke)
• Preview Mode: Click any saved image for full-size preview
• Organized Storage: Automatic categorization by detection type

3. Video Feed Controls

▶️ Play    - Start/resume video feed
⏸️ Pause   - Temporarily pause detection
⏹️ Stop    - Completely stop video feed and detection

4. Detection Monitoring

• Status Indicators: Real-time visual feedback for each detection type
• Alert System: Notifications for new detections
• Statistics Dashboard: Comprehensive detection analytics
• Historical Data: Track detection patterns over time

5. Image Saving & Gallery

• Automatic Saving: Enable in "Member's Images" dialog
• Categorized Storage: Images automatically sorted by detection type
• Gallery Browser: Intuitive interface for viewing saved detections
• Preview System: Click thumbnails for enlarged preview

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📂 Detailed Project Architecture

P8GP_G01/
│
├── 🗂️ venv/                          # Virtual environment (optional)
├── 📄 LICENSE                        # 🔒 Non-commercial use license
├── 📄 .gitignore                     # 🚫 Git exclusion rules
├── 📄 README.md                      # 📚 Main project documentation
│
├── 🗂️ Project_Picture/               # 📸 Project Screenshots & Media
│   ├── amin1.png                    # Main application interface
│   └── [additional screenshots]     # Other project images
│
└── 🗂️ UI/                            # 🏠 Main Project Hub
    │
    ├── 📁 Codes/                      # 🧠 Core Application Logic
    │   ├── __init__.py               # Package initialization
    │   ├── Application.py            # 🚀 Entry point & app launcher
    │   ├── MainWindow.py             # 🏢 Main application window
    │   ├── WelcomeScreen.py          # 👋 Animated welcome interface
    │   ├── Dashboard.py              # 📊 Central monitoring dashboard
    │   ├── VideoPlayer.py            # 📹 Video feed & playback controls
    │   ├── ControlPanel.py           # 🎛️ Detection monitoring panel
    │   ├── Notification.py           # 🔔 Alert & notification system
    │   ├── Dialogs.py                # 💬 Dialog windows & modals
    │   ├── DetectionEngine.py        # 🤖 AI detection core logic
    │   ├── CameraManager.py          # 📷 Camera handling & management
    │   └── Utilities.py              # 🛠️ Helper functions & utilities
    │
    ├── 📁 Imgs/                       # 🎨 Visual Assets
    │   ├── P1.png                    # Welcome screen background
    │   └── icon.png                  # Application icon
    │
    ├── 📁 Images/                     # 💾 Detection Output Storage
    │   ├── 📁 Masks/                 # Saved face mask detections
    │   ├── 📁 Safety_hats/           # Saved helmet detections
    │   ├── 📁 Smoking/               # Saved smoking detections
    │   ├── 📁 Fire/                  # Saved fire detections
    │   ├── 📁 smoke/                 # Saved smoke detections
    │   └── 📁 Cigarettes/            # Saved cigarette detections
    │
    ├── 📁 Models/                     # 🧠 AI Model Repository
    │   ├── Mask_Detection.pt         # Face mask detection model
    │   ├── Smoking_Detection.pt      # Smoking activity model
    │   ├── Fire_Detection.pt         # Fire detection model
    │   └── Helmet_Detection.pt       # Safety helmet model
    │
    ├── 📄 README.md                   # 📚 UI-specific documentation
    └── 📄 requirements.txt            # 📦 Python dependencies

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👥 Development Team & Credits

🎨 Lead Developer & UI Designer Amin Moniry Full-Stack Development, Programming & User Interface Design Responsible for: • Core application architecture • AI model integration • GUI design & implementation • Cross-platform compatibility • User experience optimization

🎓 Project Supervisor & Technical Advisor Saeed Shokraneh Academic Supervision & Technical Guidance Responsible for: • Project oversight & guidance • Technical review & validation • Academic supervision • Quality assurance • Strategic direction

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ℹ️ Project Information

Attribute	Value
Version	2.1
Release Date	April 25, 2025
Development Location	Tabriz, Iran 🇮🇷
Project Type	Open Source Safety Monitoring System
Primary Language	Python
GUI Framework	PyQt6
AI Framework	YOLO (Ultralytics)

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📝 Important Notes & Tips

🔧 Technical Considerations

• Camera Connection: Ensure your webcam is properly connected and accessible before launching
• Windows Users: Install pygrabber for enhanced DirectShow camera detection capabilities
• Linux Users: Optional v4l2-ctl installation for advanced camera detection (generic detection available as fallback)
• Raspberry Pi: Additional GPIO and hardware setup may be required for embedded deployment

⚡ Performance Optimization

• CPU Usage: Monitor system resources during detection operations
• Memory Management: Close unused applications for optimal performance
• Camera Resolution: Higher resolutions improve detection accuracy but increase processing load
• Model Selection: Different models have varying computational requirements

🔒 Security & Privacy

• Local Processing: All detection occurs locally - no data transmitted externally
• Image Storage: Saved images remain on local device storage
• Camera Access: Application requests camera permissions as needed

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🤝 Contributing to P8GP-G01

We welcome contributions from the community! Here's how you can help improve the project:

📋 Contribution Guidelines

1. Fork the repository to your GitHub account
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request with detailed description

🐛 Bug Reports

• Use the GitHub Issues template
• Include system information and error logs
• Provide steps to reproduce the issue
• Attach relevant screenshots if applicable

💡 Feature Requests

• Describe the proposed feature in detail
• Explain the use case and benefits
• Consider implementation complexity
• Discuss potential impacts on existing functionality

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📧 Support & Contact

📞 Get in Touch

For questions, support, or collaboration opportunities:

🌐 GitHub Repository: https://github.com/Amin-moniry-pr7

📧 Email Support: Available through GitHub profile

🐛 Issue Tracker: Use GitHub Issues for bug reports and feature requests

💬 Discussions: Join project discussions on GitHub

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🙏 Thank You

Thank you for choosing P8GP-G01 for your safety monitoring needs!

Built with ❤️ in Tabriz, Iran

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