Face Mask Detection using MobilenetV2 of Convolution Neural Network

Abstract

In light of the fact that the COVID-19 pandemic is spreading swiftly over the
earth, it is crucial to create new technologies to study and resist the disease.
Face masks and gloves are required for protection against the coronavirus, and
scientists and doctors have advised everyone to wear the mask for the whole
day. Thus, various procedures are accessible to different people wearing face
masks. Masks are advised as a basic barrier to stop respirational beads from
receiving into the air and against other people once someone is found to be
using cover hacks. Additionally, this is known as source governance. This article is based on the current understanding of respiratory beads’ function in the
escalation of COVID-19 infection. In this problem, the face mask procedure
was built using MobileNetV2. Compared to the current system, Mobile Net V2
can be used to identify face masks among individuals with greater accuracy.
The input data file contains 500 images taken from the Kaggle face mask Detection Dataset. A scene with a mix of people donning masks and without mask.
The output is a segmented picture of the same. Later, this process is improved
by using a webcam to capture real-time video. The video is then segmented into
the frame and resized as required, and the result is a video-segmented image.
The model was then run to determine whether or not individuals were wearing
masks after performing the pre-processing function. An accuracy of 80 was
used to acquire the results.