Face Mask Detection System Using AI: Computer Engineering Guide
1. Introduction
Overview of the project.
Objectives of the system: Develop an AI-based system to detect whether individuals are wearing face masks in real-time.
Scope of the system: Useful for public spaces, offices, schools, and healthcare facilities.
2. Requirements Analysis
Functional Requirements:
· - Real-time detection of face masks through camera feeds.
· - Notification or alert generation for mask violations.
· - Data logging for analytics or compliance monitoring.
· - User-friendly dashboard for administrators.
Non-Functional Requirements:
· - High detection accuracy regardless of lighting conditions.
· - Low latency for real-time detection.
· - Scalable to handle multiple camera feeds.
3. System Design
Architecture:
· - Edge computing for on-site processing or cloud-based detection for scalability.
· - Modular design with components for video capture, AI detection, and notification systems.
Data Flow Diagrams (DFDs):
· - Level 0: Overview of camera feed input and detection output.
· - Level 1: Modules for Video Processing, Detection Engine, and Alert System.
Database Design:
· - Tables: Logs, Violations, Camera Configurations, Alerts.
4. Technology Stack
Hardware:
· - Cameras for video input (CCTV or IP cameras).
· - Edge devices (e.g., NVIDIA Jetson Nano) for on-site processing.
Software:
· - AI Frameworks: TensorFlow, Keras, or PyTorch for model development.
· - Frontend: Dashboards using React.js, Angular, or a web-based framework.
· - Backend: Python (Flask/Django) or Node.js for API and data handling.
Database:
· - SQL (PostgreSQL, MySQL) or NoSQL (MongoDB, Firebase).
Cloud Services:
· - AWS, Google Cloud, or Azure for cloud processing and deployment.
5. Implementation
Data Collection and Preparation:
· - Gather datasets containing images of individuals with and without masks.
· - Annotate data for supervised learning.
· - Augment data to include variations in lighting, angles, and backgrounds.
Model Development:
· - Train a CNN (Convolutional Neural Network) model for mask detection.
· - Optimize model for real-time inference.
Frontend Development:
· - Create an intuitive dashboard for monitoring detections and violations.
· - Implement alert and notification settings in the dashboard.
Backend Development:
· - Develop APIs for video stream analysis and notification triggers.
· - Integrate with databases for logging violations.
Integration:
· - Connect hardware, AI models, and software components for seamless operation.
6. Security
Secure data transmission from cameras to servers using encryption protocols.
Implement authentication mechanisms for dashboard access.
Ensure privacy compliance with local regulations.
7. Testing
Unit Testing: Validate individual components like detection models and APIs.
Integration Testing: Ensure end-to-end communication between hardware, backend, and frontend.
System Testing: Test the system in real-world scenarios with varying conditions.
Performance Testing: Measure detection speed and accuracy.
8. Deployment
Deploy the system on cloud platforms or on-site servers based on use case.
Set up monitoring tools for system health and performance.
Train end users or administrators for system operation.
9. Maintenance and Updates
Regularly update detection models to adapt to new challenges.
Monitor system logs and address issues promptly.
Incorporate user feedback for continuous improvement.
10. Appendix
Glossary of terms.
References and additional resources.