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 arti- cle 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 Detec- tion 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.
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