Real Time Digital Face Mask Detection using MobileNet-V2 and SSD with Apache Spark

doi:10.23940/ijpe.22.08.p8.598604

Abstract

Abstract: The increasing and global spread of Coronavirus (COVID-19) has made facemasks imperative and valuable. It established new norms to our way of life with regulations that are necessary for survival. This study portrays the methodological significance of image processing using Deep Learning: MobileNet-v2 cascade for detection of the masked face and spawning face embedding. It achieves the best results for larger datasets as MobileNet-V2 is a convolutional semantic network with a depth of about 53 layers, meanwhile, the application of similar methods on smaller datasets proves challenging. This paper paves a path of exploring detection on the basis of the Single Shot Detector (SSD) algorithm that introduces a channel attention mechanism to improve the ability of the model to express salient features while simultaneously utilizing information of different feature levels optimizing the function loss. It also sheds light on the resultant output, which creates a large chunk of data categorized as big data. The algorithm shows final experimental results predicting the goal of face recognition and mask detention as successful and effective with an accuracy of the results ranging between 90-95%.

Key words: COVID-19, MobileNet-v2, deep learning, big data, SSD

K. Lavanya, Smrithi Prakash, Yash Gedam, Altamash Aijaz, and L. Ramanathan. Real Time Digital Face Mask Detection using MobileNet-V2 and SSD with Apache Spark [J]. Int J Performability Eng, 2022, 18(8): 598-604.

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