Face Mask Detection

Abstract

1. Introduction

Amid the ongoing COVID-19 pandemic, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. The absence of large datasets of ‘with_mask’ images has made this task cumbersome and challenging.

Face Mask Detection System built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in real-time video streams.

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2. Objective

Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras

This project demonstrates how Jupyter Notebook, Python, Keras can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

• Caffe-based face detector
• TensorFlow
• MobileNetV2
• Raspberry Pi 4 4GB with a case
• 5MP OV5647 PiCamera from Arducam

5. System Requirements

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

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/chandrikadeb7/Face-Mask-Detection.git
cd Face-Mask-Detection

1. Clone the repo
2. Change your directory to the cloned repo
3. Create a Python virtual environment named 'test' and activate it
4. Now, run the following command in your Terminal/Command Prompt to install the libraries required
5. With NOOBS, use the recommended operating system
6. Setup the PiCamera
7. Assemble the PiCamera case from Arducam using documentation/Arducam-Case-Setup.pdf or https://www.arducam.com/docs/cameras-for-raspberry-pi/native-raspberry-pi-cameras/5mp-ov5647-cameras/
8. Attach your PiCamera module to the Raspberry Pi and enable the camera

$ git clone https://github.com/chandrikadeb7/Face-Mask-Detection.git

$ cd Face-Mask-Detection

$ virtualenv test

$ source test/bin/activate

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 chandrikadeb7 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.