Detection of Face Mask Wearing for COVID-19 Protection based on Transfer Learning and Classic CNN Model
19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2230750
ABSTRACT
In 2020, COVID-19 swept the world. To prevent the spread of the outbreak, it is crucial to ensure that everyone wears a mask during daily travel and in public places. However, relying on human inspection alone is inevitably negligent and there is a potential risk of cross-contamination between people. Automated detection by means of cameras and artificial intelligence becomes a technical solution. By training convolutional neural networks, image recognition can be implemented and image classification can be performed as a solution to the target mask-wearing detection problem. To this end, in this thesis, three typical convolutional neural network architectures, VGG-16, Inception V3, and DenseNet-121, are used as models based on deep learning to investigate the mask-wearing detection problem by using transfer learning ideas. By building six different models and comparing the performance of different typical network architectures on the same dataset using two transfer learning methods, feature extraction and fine-tuning, we can conclude that DenseNet-121 is the typical architecture with the best performance among the three networks, and fine-tuning has better transfer ability than feature extraction in solving the target mask wearing detection problem. © 2022 IEEE.
convolutional neural network; deep learning; Fine-tuning; transfer learning; Contamination; Convolution; COVID-19; Extraction; Face recognition; Feature extraction; Learning systems; Network architecture; Risk perception; CNN models; Detection problems; Face masks; Features extraction; Fine tuning; Performance; Public places; Convolutional neural networks
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
/
Randomized controlled trials
Language:
English
Journal:
19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022
Year:
2022
Document Type:
Article
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