Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT

During the current outbreak of the COVID-19 pandemic, controlling and decreasing the possibilities of infections are massively required. One of the most important solutions is to use Artificial Intelligence (AI), which combines both fields of deep learning (DL) and the Internet of Things (IoT). The...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Omar Mowaffak Alsaydia, Noor Raad Saadallah, Fahad Layth Malallah, Maan A. S. AL-Adwany
Formato:	article
Lenguaje:	EN RU UK
Publicado:	PC Technology Center 2021
Materias:	covid-19 computer vision machine learning deep learning remote control embedded system big data iot Technology (General) T1-995 Industry HD2321-4730.9
Acceso en línea:	https://doaj.org/article/36f9bbd268a045c6a2ed5fe64c41665b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:36f9bbd268a045c6a2ed5fe64c41665b
record_format	dspace
spelling	oai:doaj.org-article:36f9bbd268a045c6a2ed5fe64c41665b2021-11-04T14:06:13ZLimiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT1729-37741729-406110.15587/1729-4061.2021.238359https://doaj.org/article/36f9bbd268a045c6a2ed5fe64c41665b2021-10-01T00:00:00Zhttp://journals.uran.ua/eejet/article/view/238359https://doaj.org/toc/1729-3774https://doaj.org/toc/1729-4061During the current outbreak of the COVID-19 pandemic, controlling and decreasing the possibilities of infections are massively required. One of the most important solutions is to use Artificial Intelligence (AI), which combines both fields of deep learning (DL) and the Internet of Things (IoT). The former one is responsible for detecting any face, which is not wearing a mask. Whereas, the latter is exploited to manage the control for the entire building or a public area such as bus, train station, or airport by connecting a Closed-Circuit Television (CCTV) camera to the room of management. The work is implemented using a Core-i5 CPU workstation attached with a Webcam. Then, MATLAB software is programmed to instruct both Arduino and NodeMCU (Micro-Controller Unit) for remote control as IoT. In terms of deep learning, a 15-layer convolutional neural network is exploited to train 1,376 image samples to generate a reference model to use for comparison. Before deep learning, preprocessing operations for both image enhancement and scaling are applied to each image sample. For the training and testing of the proposed system, the Simulated Masked Face Recognition Dataset ( SMFRD) has been exploited. This dataset is published online. Then, the proposed deep learning system has an average accuracy of up to 98.98 %, where 80 % of the dataset was used for training and 20 % of the samples are dedicated to testing the proposed intelligent system. The IoT system is implemented using Arduino and NodeMCU_TX (for transmitter) and RX (for receiver) for the signal transferring through long distances. Several experiments have been conducted and showed that the results are reasonable and thus the model can be commercially appliedOmar Mowaffak AlsaydiaNoor Raad SaadallahFahad Layth MalallahMaan A. S. AL-AdwanyPC Technology Centerarticlecovid-19computer visionmachine learningdeep learningremote controlembedded systembig dataiotTechnology (General)T1-995IndustryHD2321-4730.9ENRUUKEastern-European Journal of Enterprise Technologies, Vol 5, Iss 2 (113), Pp 29-36 (2021)
institution	DOAJ
collection	DOAJ
language	EN RU UK
topic	covid-19 computer vision machine learning deep learning remote control embedded system big data iot Technology (General) T1-995 Industry HD2321-4730.9
spellingShingle	covid-19 computer vision machine learning deep learning remote control embedded system big data iot Technology (General) T1-995 Industry HD2321-4730.9 Omar Mowaffak Alsaydia Noor Raad Saadallah Fahad Layth Malallah Maan A. S. AL-Adwany Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT
description	During the current outbreak of the COVID-19 pandemic, controlling and decreasing the possibilities of infections are massively required. One of the most important solutions is to use Artificial Intelligence (AI), which combines both fields of deep learning (DL) and the Internet of Things (IoT). The former one is responsible for detecting any face, which is not wearing a mask. Whereas, the latter is exploited to manage the control for the entire building or a public area such as bus, train station, or airport by connecting a Closed-Circuit Television (CCTV) camera to the room of management. The work is implemented using a Core-i5 CPU workstation attached with a Webcam. Then, MATLAB software is programmed to instruct both Arduino and NodeMCU (Micro-Controller Unit) for remote control as IoT. In terms of deep learning, a 15-layer convolutional neural network is exploited to train 1,376 image samples to generate a reference model to use for comparison. Before deep learning, preprocessing operations for both image enhancement and scaling are applied to each image sample. For the training and testing of the proposed system, the Simulated Masked Face Recognition Dataset ( SMFRD) has been exploited. This dataset is published online. Then, the proposed deep learning system has an average accuracy of up to 98.98 %, where 80 % of the dataset was used for training and 20 % of the samples are dedicated to testing the proposed intelligent system. The IoT system is implemented using Arduino and NodeMCU_TX (for transmitter) and RX (for receiver) for the signal transferring through long distances. Several experiments have been conducted and showed that the results are reasonable and thus the model can be commercially applied
format	article
author	Omar Mowaffak Alsaydia Noor Raad Saadallah Fahad Layth Malallah Maan A. S. AL-Adwany
author_facet	Omar Mowaffak Alsaydia Noor Raad Saadallah Fahad Layth Malallah Maan A. S. AL-Adwany
author_sort	Omar Mowaffak Alsaydia
title	Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT
title_short	Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT
title_full	Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT
title_fullStr	Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT
title_full_unstemmed	Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT
title_sort	limiting covid-19 infection by automatic remote face mask monitoring and detection using deep learning with iot
publisher	PC Technology Center
publishDate	2021
url	https://doaj.org/article/36f9bbd268a045c6a2ed5fe64c41665b
work_keys_str_mv	AT omarmowaffakalsaydia limitingcovid19infectionbyautomaticremotefacemaskmonitoringanddetectionusingdeeplearningwithiot AT noorraadsaadallah limitingcovid19infectionbyautomaticremotefacemaskmonitoringanddetectionusingdeeplearningwithiot AT fahadlaythmalallah limitingcovid19infectionbyautomaticremotefacemaskmonitoringanddetectionusingdeeplearningwithiot AT maanasaladwany limitingcovid19infectionbyautomaticremotefacemaskmonitoringanddetectionusingdeeplearningwithiot
_version_	1718444830913200128

Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT

Ejemplares similares