EfficientNet-B0 Model for Face Mask Detection Based on Social Information Retrieval

Moolchand Sharma, Harsh Gunwant, Pranay Saggar, Luv Gupta and Deepak Gupta
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Moolchand Sharma: Maharaja Agrasen Institute of Technology, India
Harsh Gunwant: Maharaja Agrasen Institute of Technology, India
Pranay Saggar: GTBIT, India
Luv Gupta: Sharda University, India
Deepak Gupta: Maharaja Agrasen Institute of Technology, India

International Journal of Information System Modeling and Design (IJISMD), 2022, vol. 13, issue 7, 1-15

Abstract: The world was introduced to the term coronavirus at the end of 2019, following which everyone was thrown into stress and anxiety. The pandemic has been a complete disaster, wreaking devastation and resulting in a significant loss of human life throughout the world. The governments of various countries have issued guidelines and protocols to be followed for stopping the surge in cases (i.e., wearing masks). Amidst all this chaos, the only weapon is technology. So, the detection of face masks is important. The authors utilized a dataset that included images of individuals in society wearing and not wearing masks. They gathered the information required to train a model by using deep networks like EfficientNetB0, MobileNetV2, ResNet50, and InceptionV3. With EfficientNet-B0, they have been able to achieve an accuracy of 99.70% on a two-class classification issue. These methods make face mask detection easier and help in knowledge discovery. These technological breakthroughs may aid in information retrieval as well as help society and guarantee that such a healthcare disaster does not occur again.

Date: 2022
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