Master of technology in information technology department of information science and

2.1 SUMMARY OF THE SURVEY 
In the rise of Covid-19 cases all over the world, it is mandatory to 
follow the guidelines provided by WHO and local health authorities. The 
above cited paper were useful in visualization and understanding the flow of 
the entire project and also designing of its architecture, input and its format to 
be given for each module along with expected output were identified. The 
above cited papers are focused at one point in detecting people not wearing a 
mask. With reference to all these papers, our project is also focused on 
detecting people not wearing a face mask using MobileNetV2. 
 
 
 
 
 

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CHAPTER 3
 
SYSTEM DESIGN 
Software design is a way through which the requirements can be 
converted into a proper representation. This representation can be used to 
create architecture and detailed design diagrams through which further process 
of the project can be carried out.
 3.1 EXISTING SYSTEM 

 
The existing system to detect face masks is developed using yolov3. In 
the existing system, they introduced a backbone network which can allocate 
more resources and employed Glou and focal loss to accelerate the training 
process and improve performance. It produces an accuracy of 86%. The 
accuracy can be improved while using the application with different 
algorithms.
3.2 PROPOSED SYSTEM 
 
In the proposed system the face mask detection is done using 
MobileNet V2. Use of MobileNet V2 can be used to detect face masks in a 
group of people and produce better accuracy compared to the existing system. 
Provide an image of a few people wearing a mask and not wearing a mask as 
input dataset and the segmented image of the same is obtained as output. Then 
the model is implemented using a webcam where the video is read by frame 
and resized as necessary. Then, the preprocessing function is called to get the 
result of people wearing a mask and not wearing a mask along with the 
accuracy in percentage.

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