Face Mask Detection Raspberry Pi 64 Bits

Face mask detection on Raspberry Pi 4

AI & Machine LearningC++BSD-3-Clause

Abstract

Face Mask Detection Raspberry Pi 64 Bits is an open-source AI & Machine Learning project. Face mask detection on Raspberry Pi 4. A new and superior version with only TensorFlow Lite for a bare Raspberry Pi see GitHub. It is built using C++, Deep Learning. The complete source code is publicly available on GitHub under the BSD 3-Clause "New" or "Revised" License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

A new and superior version with only TensorFlow Lite for a bare Raspberry Pi see GitHub

2. Objective

Face mask detection on Raspberry Pi 4

This project demonstrates how C++, Deep Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

C++Deep Learning
  • A raspberry Pi 4 with a 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. Install 64-bit OS
  • The Paddle Lite framework installed. Install Paddle
  • The Tencent ncnn framework installed. Install ncnn
  • OpenCV 64 bit installed. Install OpenCV 4.5
  • Code::Blocks installed. ($ sudo apt-get install codeblocks)

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Arduino IDE / PlatformIO or a C++ compiler (g++)
  • Target board (e.g. Arduino, ESP32) where applicable
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Qengineering/Face-Mask-Detection-Raspberry-Pi-64-bits.git
cd Face-Mask-Detection-Raspberry-Pi-64-bits

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by Qengineering and published on GitHub under the BSD 3-Clause "New" or "Revised" License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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