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BHOS Tezisler 2022 17x24sm

THE 3
rd
 INTERNATIONAL SCIENTIFIC CONFERENCES OF STUDENTS AND YOUNG RESEARCHERS 
dedicated to the 99
th
anniversary of the National Leader of Azerbaijan Heydar Aliyev
172
At test time we multiply the conditional class probabilities and the 
individual box confidence predictions, which gives us class-specific 
confidence scores for each box: 
[2]
Results of the Simulation 
Firstly, the main steps are represented in the figure 1 below: 
Figure 11.
Steps
In the simulation, object detection is done by using trained YOLOv3 on 
custom dataset. After preparing the custom dataset in the YOLOv3 format, 
YOLOv3 is trained and tested, predictions are displayed. Simulation outputs 
for the different input images are displayed below: 
Figure 12.
Simulation results according to input images 
As it can be seen from figure 2, predictions are 100% accurate. After 
the ID verification, the person’s vaccine passport and current COVID status 
should be checked according to his unique id in the database.
Conclusion 
The accuracy and performance of real-time face recognition using the 
YOLOv3 model has been evaluated multiple times. The model performed as 
planned and can be further developed depending on the application field. 
Moreover, it is better to use YOLOv3 for only extracting faces because it 
requires a lot of images per person for training. Therefore, obtained image of 
face by YOLOv3 is sent to CNN in order to identify the person. CNN model 
would require less images per person compared to YOLOv3. 
References
 
[1] Real-Time Object Detection using YOLO: A review, Upulie Handalage, Lakshini 
Kuganandamurthy, 2021, 3-4 
[2] You Only Look Once: Unified, Real-Time Object Detection Joseph Redmon, Santosh 
Divvala, Ross Girshick, Ali Farhadi, 2016, 1-2 



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