Face mask Dectection using YOLO

2. INTRODUCTION
●Affecting the health and lives of masses COVID-19
has concerned strict measures to be followed to stop
thespread of disease.
●The proposed model may be enhanced
using including various parameters like
people’s count, social distance, and
temperature measurement.
●People wear face masks once they go out of their
homes and authorities strictly
make sure thatpeople are wearing face masks
while they're in groups and public places
●A mask
detector system
is implemented
to test this.
mask detection
means to spot
whether an
individual is
wearing a mask
or not.

3. FACE MASK DETECTION
ALGORITHM DEVELOPMENT
The algorithm for the
detection of persons with
face masks is discussed
intimately.YOLO object
detection algorithm is
used for the detection of
persons with masks and
without a mask.
Here yolo workflow is
discussed step by step.

4. YOLOmay bea popular objectdetection
algorithm because itachieves high
accuracy while it'salso abletorun in real-
time.
• YOLO is extremely fast.
•YOLO scans the complete image during training
and also during testing. So, it implicitly encodes
contextual information about classes still as their
appearance.
•YOLO learns generalizable representations of
objects so that when it's trained on natural
images and tested, the algorithm performs
excellently compared to other top detection
methods.
BENEFITS

5. YOLOYOLO WORK FLOW

6. Figure
1.1
Input
Image
EXPERIMENTS RESULTS
AND DISCUSSION:
Figure
1.2
Input
Image
The experimental
results obtained
during this project
work are discussed
here. The results are
analyzed at various
levels. YOLOv3 images
are needed and also
the identification of
persons wearing and
not wearing masks is
processed. figure 5.1
and figure 5.2 live
stream input video is
received and they are
processed frame by
frame.

7. YOLO-OBJECT DETECTION
ALGORITHM
●Deep Learning consists of an enormous
number of neural networks that use the
multiple cores of a process of a computer
and video processing cards to manage the
neural network’s neuron which is
categorized as one node.

8. Deeplearning is usedin numerous
applications becauseof its popularity,
especially within thefield of medication
andagriculture. HereYOLOdeeplearning
techniqueis usedtospotpersons
wearing andnotwearing face masks.
Joseph Redmonetal.introducedYou
lookonly once also referred toas YOLOin
2015.
Our model uses YOLOv3 and
it provides good results
regarding object
classification and detection.
within the previous version
of Yolov2 Darknet-19 is used.
Yolov3 uses darknet-53.
Darknet may be a framework
used for training neural
networks written in C
language.

9. The Mission:
Hereafter detection,
supported status the LED
will glow.The table given
below shows the LED
activation scheme.
TABLE:
LED Activation Scheme

10. ●Once the detection is completed then
matters or condition is displayed whether
safe, warning, or dangerous. Based upon
the condition intimation light glow
activated. Figure 1.3 and figure 1.4 show
the output images.

11. Figure 1.3 Output Image
Figure 1.4 Output Image

12. Conclusion
●In this work of mask detection,
we've used YOLOv3 to detect the
persons with masks and without
masks with good efficiency and
sent an intimation message to
authorized persons
●Its performance is well in images and
our detection results were also quite
good.This detection may also be used
for a video stream or camera-fed inputs. to
urge improved performance and speed,
detection algorithm
●This project is going to be very
helpful and may be implemented in
hospitals, airports, schools, colleges,
offices, shops, malls, theaters,
temples, apartments and might even
be implemented for Covid free event
management.