🦺 Autonomous Workers Safety System for On-Site Hazard Prevention

📌 Overview

This project is a Final Year Project (FYP) for BSCS, focusing on construction site safety using AI, Computer Vision, and Robotics.

The system integrates YOLOv8 for PPE detection, facial recognition for attendance tracking, and a Arduino-based autonomous patrolling robot to monitor construction sites. It stores attendance, violation logs, and analytics in an SQLite database with a Flask-based web dashboard for visualization and management.

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✨ Key Features

• 🔍 PPE Detection (YOLOv8) – Detects helmets, vests, and other PPE in real-time.
• 🧑‍🤝‍🧑 Facial Recognition Attendance – Automates employee attendance marking.
• 🤖 Autonomous Patrolling Robot (Arduino) – Patrols the site, captures video, and detects hazards.
• 📊 Web Dashboard (Flask + HTML Templates) – Displays analytics, attendance, violations, employee details, and salary reports.
• 🗄️ Database (SQLite) – Stores attendance logs, PPE violations, and employee records.
• 📷 Violation Evidence – Captures and stores images of violations in /static/violation_images.

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📂 Project Structure

Autonomous-Workers-Safety-System/
│
├── Notebook/
│   └── Construction_Site_Workers_PPE_Detection.ipynb   # Training/Testing Notebook
│
├── Path memorizer robot/
│   ├── path_memorizer arduino code/
│   │   └── path_memorizer.ino                          # Arduino code for robot
│   └── Circuit diagram.jpg                             # Robot wiring diagram
│
├── Results/                                            # Model results & evaluation
│
├── static/                                             # Static assets
│   ├── employees_faces/                                # Employee face dataset
│   ├── images/                                         # General images
│   ├── models/                                         # Trained YOLOv8 models
│   ├── reports/                                        # Reports & charts
│   └── violation_images/                               # Captured violation evidence
│
├── templates/                                          # Flask HTML templates
│   ├── about.html
│   ├── add_employee.html
│   ├── analytics.html
│   ├── attendance.html
│   ├── base.html
│   ├── detection.html
│   ├── employees.html
│   ├── index.html
│   ├── salary_details.html
│   └── violations.html
│
├── app.py                                              # Flask app entry point
├── requirements.txt                                    # Python dependencies
├── safety_system.db                                    # SQLite database
├── LICENSE
└── README.md

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⚙️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/username/autonomous-safety-system.git
cd autonomous-safety-system

2️⃣ Create Virtual Environment (Optional but Recommended)

python -m venv venv
source venv/bin/activate   # Linux/Mac
venv\Scripts\activate      # Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run the Flask Web App

python app.py

Now open http://127.0.0.1:5000/ in your browser.

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🖥️ Web Dashboard Pages

• Home (index.html) – Overview of the system.
• Add Employee (add_employee.html) – Register new workers.
• Attendance (attendance.html) – View daily attendance logs.
• Analytics (analytics.html) – Charts and reports of safety compliance.
• Detection (detection.html) – Real-time PPE detection stream.
• Violations (violations.html) – List of recorded violations with evidence.
• Employees (employees.html) – Employee database.
• Salary Details (salary_details.html) – Worker salary/attendance report.

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🛠️ Technologies Used

• Computer Vision: YOLOv8, OpenCV, Face Recognition
• Backend: Flask (Python)
• Database: SQLite
• Frontend: HTML, CSS (Flask templates)
• Robotics: Arduino
• Programming: Python, Arduino C

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📈 Results & Reports

• Real-time detection accuracy using YOLOv8.
• Attendance tracking via facial recognition.
• Violation images captured & stored for audits.
• Reports generated and displayed in analytics dashboard.

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🚀 Future Scope

• Cloud integration for large-scale deployment.
• IoT sensors for hazard detection (fire, gas leaks).
• SMS/Email alerts for violations.
• Multi-robot patrolling.

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👨‍💻 Author

Ahmed Islam
BSCS – Final Year Project

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