An Efficient Face Mask Detector with PyTorch and Deep Learning

Basha, CMAK. Zeelan and Pravallika, B.N. Lakshmi and Shankar, E. Bharani (2021) An Efficient Face Mask Detector with PyTorch and Deep Learning. EAI Endorsed Transactions on Pervasive Health and Technology, 7 (25). e4. ISSN 2411-7145

[thumbnail of eai.8-1-2021.167843.pdf]
Available under License Creative Commons Attribution No Derivatives.

Download (2MB) | Preview


INTRODUCTION: The outbreak ofacoronavirus disease in 2019 (COVID-19) has created a global health epidemic that has had a major effect on the way we view our environment and our daily lives. The Covid-19 affected numbers are rising at a tremendous pace. Because of that, many countries face an economic catastrophe, recession, and much more. One thing we should do is to separate ourselves from society, remain at home, and detach ourselves from the outside world. But that's no longer a choice, people need to earn to survive, and nobody can remain indefinitely within their homes. As a precaution, people should wear masks while keeping social distance, but some ignore such things and walk around.

OBJECTIVES: To develop aFace Mask Detector with OpenCV, PyTorch, and Deep Learning that helps to detect whether or not a person wears a mask.

METHODS: A Neural Network model called ResNet is trained on the dataset. Furthermore, this work makes use of the inbuilt Face Detector after training. Finally, we predict whether or not a person is wearing a mask along with the percentage of the face covered or uncovered.

RESULTS: The validation results have been proposed to be 97% accurate when compared to applying different algorithms.

CONCLUSION: This Face Mask Detection System was found to be apt for detecting whether or not people wear masks in public places which contribute to their health and also to the health of their contacts in this COVID-19 pandemic.

Item Type: Article
Uncontrolled Keywords: COVID-19, face mask, resnet, pytorch, RMFRD
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 04 Feb 2021 14:25
Last Modified: 04 Feb 2021 14:25

Actions (login required)

View Item
View Item