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◈ Overview

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

The system features a PyQt6-powered graphical interface with real-time monitoring, automated image saving, and detection analytics. It supports multiple camera sources across Windows, Linux, and macOS.

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◈ Screenshots

▶ Main application interface with real-time detection

▶ Helmet, mask, and smoking detection in action

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◈ Detection Capabilities

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

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◈ System Requirements

Component	Minimum	Recommended
OS	Windows 10, Ubuntu 18.04+, macOS 10.15+	Latest stable versions
Python	3.10+	3.11+
RAM	4 GB	8 GB+
Storage	2 GB	5 GB+
Camera	USB 2.0 webcam	USB 3.0 HD webcam
CPU	Dual-core 2.0 GHz	Quad-core 2.5 GHz+

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◈ Installation

  Clone the project and navigate into the directory.

git clone https://github.com/Amin-Moniry/P8GP-G01.git
cd P8GP_G01

  Create and activate a Python virtual environment.

python -m venv venv

# Windows
venv\Scripts\activate

# Linux / macOS
source venv/bin/activate

  Install all required packages.

cd UI
pip install -r requirements.txt

  Ensure model files are present in UI/Models/.

Model	Status
Fire_Detection.pt	✅ Required
Helmet_Detection.pt	✅ Required
Mask_Detection.pt	✅ Required
Smoking_Detection.pt	⚠️ Optional

  Launch the application.

python Codes/Application.py

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◈ Key Dependencies

torch==2.3.1
opencv-python==4.9.0.80
numpy==1.26.4
ultralytics==8.1.0
PyQt6==6.6.1
pygrabber==0.2          # Windows only — enhanced camera detection

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◈ Project Structure

P8GP_G01/
├── LICENSE
├── .gitignore
├── README.md
├── Project_Picture/         # Screenshots
└── UI/
    ├── Codes/               # Application source code
    │   ├── Application.py   # Entry point
    │   ├── MainWindow.py
    │   ├── DetectionEngine.py
    │   ├── CameraManager.py
    │   └── ...
    ├── Models/              # AI model files (.pt)
    ├── Images/              # Detection output storage
    ├── Imgs/                # UI assets
    └── requirements.txt

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◈ Usage

1. Welcome Screen — Launch the app and click "Get Started"
2. Select Camera — Choose from available devices in the control panel
3. Start Detection — Press ▶ Play to begin real-time monitoring
4. View Results — Detection overlays and confidence scores appear on the live feed
5. Save Images — Enable image saving to automatically categorize detections by type
6. Browse Gallery — Review saved detections organized by category (Masks, Helmets, Fire, etc.)

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◈ Project Information

Attribute	Value
Version	2.1
Release Date	April 25, 2026
Development Location	Tabriz, Iran 🇮🇷
Primary Language	Python
GUI Framework	PyQt6
AI Framework	YOLO (Ultralytics)

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◈ Development Team

Role	Name
Lead Developer & UI Designer	Amin Moniry (AminTivanix2)
Project Supervisor & Technical Advisor	Saeed Ssh

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◈ License

This project is protected under a Non-Commercial License.

• ✅ Permitted: Personal use, education, research, open-source contributions
• ❌ Prohibited: Commercial use, revenue generation, corporate deployment
• 📋 Required: Attribution to original authors and license inclusion in distributions

© 2026 Amin Moniry (AminTivanix2) — All Rights Reserved

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◈ Contact