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Using deep learning for COVID-19 control: Implementing a convolutional neural network in a facemask detection application
2021 International Conference on Smart Applications, Communications and Networking, SmartNets 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1483770
ABSTRACT
The ongoing COVID-19 pandemic has changed people's lives in ways that many would not have predicted. In the days, weeks and months since mandatory lockdowns and restrictions came into effect worldwide, people have had to adjust their daily lives in an effort to slow and restrict the spread of the virus - like regularly sanitising their hands, maintaining social distancing in crowded places, and wearing facemasks. The latter is contentious for some but has been a necessary deterrent in slowing the spread of this virus. There is potential for utilising technology as a supplementary deterrent and monitoring tool to help detect non-compliance of mask wearing. This research investigates the efficacy of AI for such purposes, exploring the applicability of a Convolutional Neural Network (CNN), for predicting if a person in a real time video feed is wearing a facemask. A dataset of over 10, 000 images was created to effectively evaluate this research. The CNN developed was tested against the validation dataset to evaluate its performance, the model demonstrated 98.47% accuracy on a varied and balanced dataset. © 2021 IEEE.

Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: 2021 International Conference on Smart Applications, Communications and Networking, SmartNets 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: 2021 International Conference on Smart Applications, Communications and Networking, SmartNets 2021 Year: 2021 Document Type: Article