Research on Mask Wearing Detection Based on Faster RCNN
5th International Conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2022
; : 629-633, 2022.
Article
in English
| Scopus | ID: covidwho-2161367
ABSTRACT
In the context of the global raging of the new coronavirus (COVID-19), to effectively prevent the spread of the new coronavirus in the crowd, many places require the wearing of masks in public places. In response to this problem, this paper proposes a mask wearing detection based on the FasterRCNN algorithm. The method uses ResNet-50 to extract convolution features and selects high-quality suggestion boxes through NMS (non-maximum suppression), which increases the detection of incorrectly wearing masks, which can play a reminder role in practical applications and further improve the prevention of epidemics, and the final experiments show that the wearing of masks can be accurately and efficiently detected through the steps of feature extraction and prediction frame generation. © 2022 IEEE.
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
5th International Conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2022
Year:
2022
Document Type:
Article
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