Face Mask Recognition Based on YOLOv3-tiny
3rd International Conference on Electronic Communication and Artificial Intelligence, IWECAI 2022
; : 507-511, 2022.
Article
in English
| Scopus | ID: covidwho-1831841
ABSTRACT
The use of computer vision to realize non-contact face mask wearing testing and identify the standardization of wearing can improve the efficiency of mask wearing inspection, which is of great significance to reduce the spread of COVID-19. Based on the YOLO v3 framework in deep learning, this project conducts an in-depth study on the accuracy and efficiency of face mask detection, introduces its structure and principle, and explores and experiments its application based on TensorFlow. The evaluation and identification rate of the experiment was 78∗, which verified the feasibility and practicability of the method. © 2022 IEEE.
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
3rd International Conference on Electronic Communication and Artificial Intelligence, IWECAI 2022
Year:
2022
Document Type:
Article
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