My project face mask detection for Covid19 safety please reading the project and like, share & follow me

1. PROJECT
REPORT
Face mask
detection

2. SONESH KUMAR
ASHISH KUSHWAH
Project
member

3. Software part
And
Hardware part
Software part:-Sonesh kumar
Hrdware part:-Ashish kushwah

4. REQUIRED COMPONENT
 Raspberry pi
 Pi camera
 GSM module
 Power supply

5. PROJECT INTRODUSE
 In these tough COVID-19 times, wouldn’t it be
satisfying to do something related to it? I decided
to build a very simple and basic Convolutional
Neural Network (CNN) model using TensorFlow
with Keras library and OpenCV to detect if you
are wearing a face mask to protect yourself.
 I am going to use these images to build a CNN
model using TensorFlow to detect if you are
wearing a face mask by using the webcam of your
PC. Additionally, you can also use your phone’s
camera to do the same.

6. ARCHITECTURE OF THE SYSTEM

7. BLOCK DIAGRAM DESCRIPTION
1.Power Supply:-
This system requires 5V, 1A power
supply. The raspberry pi model B has
the special connection provided. Using
that USB connection the power supply can be provided.
2.Camera:-
It is used to capture an image of
crops, it is directly connected to the
raspberry Pi 3 Model B. There are two ways
to connect camera to raspberry Pi 3 model
B. First one is through USB port and second
is 15 pin header provided for camera
interface of raspberry Pi3.
3.Raspberry PI:-
Raspberry Pi is small size module
like a small computer. The image captured
by camera is sent to the Raspberry Pi. Using
open CV library, the image is processed and
detected by the Raspberry Pi.

8. 4.GSM:-
It is used to send the SMS to the
owner of the system, this message consists
of name of the disease detected by the
processor.
5.Monitor as display:-
The monitor is used to display the detected
disease name and also the pesticide name.
6.E-MAIL:-
The email is sent to the owner of the
system. This email contains the disease
detected and name of the pesticide.

9. HOW TO WORK

10. APPLICATION
 Face Mask detection cameras are placed in
the area to be checked and put into
operation.
 Face Mask Detection Platform uses Artificial
Network to recognize if a user is not wearing
a mask.
 Educational institutes
 Hospitals

11. ADVANTAGE
 Automated Image Recognition
 Human-Computer Interaction
 Deployment in Security Measures
 Security Through Biometric Authentication

12. CONCLUSION
 The current study used OpenCV, Pytorch
and CNN to detect whether people were
wearing face masks or not.
 MobileNetV2 was used to build the mobile
version of the same.
 This specific model could be used as a use
case for edge analytics.