IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus The excessive use of manual disinfection systems has also become a source of infection That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19 We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach We achieved the highest accuracy of 99 81% while using VGG-16 and the second highest accuracy of 99 6% using MobileNetV2 in the mask detection and classification module We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19

[1]  Shiming Ge,et al.  Detecting Masked Faces in the Wild with LLE-CNNs , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Ahmed Farouk,et al.  Secure Medical Data Transmission Model for IoT-Based Healthcare Systems , 2018, IEEE Access.

[3]  Jianyun Lu,et al.  COVID-19 Outbreak Associated with Air Conditioning in Restaurant, Guangzhou, China, 2020 , 2020, Emerging infectious diseases.

[4]  T. Greenhalgh,et al.  Face masks for the public during the covid-19 crisis , 2020, BMJ.

[5]  Dongxiao Li,et al.  Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19 , 2020, Sensors.

[6]  Shiv Govind Singh,et al.  PREFACE on the Special Issue ‘Technologies for Fighting COVID-19’ , 2020, Transactions of the Indian National Academy of Engineering.

[7]  Saad Motahhir,et al.  Proposed Design of Walk-Through Gate (WTG): Mitigating the Effect of COVID-19 , 2020, Applied System Innovation.

[8]  Centers for Disease Control and Prevention CDC COVID-19 Response Team.  Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) — United States, February 12–March 16, 2020 , 2020, MMWR. Morbidity and mortality weekly report.

[9]  Ali Narin,et al.  Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks , 2020, Pattern Analysis and Applications.

[10]  R. Dingwall,et al.  Face masks for the public during Covid-19: an appeal for caution in policy , 2020 .

[11]  B. Liu,et al.  Aerosol and Surface Distribution of Severe Acute Respiratory Syndrome Coronavirus 2 in Hospital Wards, Wuhan, China, 2020 , 2020, Emerging infectious diseases.

[12]  Cdc Covid- Response Team.  Preliminary estimates of the prevalence of selected underlying health conditions among patients with coronavirus disease 2019 — United States, February 12–March 28, 2020 , 2020 .

[13]  Arni S. R. Srinivasa Rao,et al.  Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine , 2020, Infection Control & Hospital Epidemiology.

[14]  Narinder Singh Punn,et al.  Face Mask Detection using Transfer Learning of InceptionV3 , 2020, BDA.

[15]  P. Nagrath,et al.  SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2 , 2020, Sustainable Cities and Society.

[16]  Guy Cazuguel,et al.  TeleOphta: Machine learning and image processing methods for teleophthalmology , 2013 .

[17]  W. Hugentobler,et al.  Seasonality of Respiratory Viral Infections. , 2020, Annual review of virology.

[18]  E. Dong,et al.  An interactive web-based dashboard to track COVID-19 in real time , 2020, The Lancet Infectious Diseases.

[19]  Rao Asrs,et al.  Identification of COVID-19 Can be Quicker through Artificial Intelligence framework using a Mobile Phone-Based Survey in the Populations when Cities/Towns Are under Quarantine , 2020 .

[20]  P Lafiosca,et al.  Review of non-contact methods for automated aircraft inspections , 2020 .

[21]  Shaik Asif Hussain,et al.  A real time face emotion classification and recognition using deep learning model , 2020, Journal of Physics: Conference Series.

[22]  Yang Yu,et al.  Deep Learning-Based Vibration Signal Personnel Positioning System , 2020, IEEE Access.

[23]  Mohammad Farukh Hashmi,et al.  Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning , 2020, Diagnostics.

[24]  Gunasekaran Manogaran,et al.  A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic , 2020, Measurement.

[25]  Jordan J. Bird,et al.  A Study on CNN Transfer Learning for Image Classification , 2018, UKCI.

[26]  Dylan H. Morris,et al.  Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1 , 2020, The New England journal of medicine.

[27]  Nour Eldeen M. Khalifa,et al.  Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection , 2020, Sustainable Cities and Society.