Real-Time Mask Identification for COVID-19: An Edge-Computing-Based Deep Learning Framework

Kong, Xiangjie; Wang, Kailai; Wang, Shupeng; Wang, Xiaojie; Jiang, Xin; Guo, Yi; Shen, Guojiang; Chen, Xin; Ni, Qichao

doi:10.1109/jiot.2021.3051844

Cited by 91 publications

(28 citation statements)

References 37 publications

(57 reference statements)

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“…Xiangjie Kong et al [11] forward an edge figuring based cover recognizable proof structure (ECMask) to help general wellbeing safeguards, which can guarantee constant execution on the low-power camera gadgets of transports. Video reclamation, face location, and cover recognizable proof are its three principal stages.…”

Section: Literature Reviewmentioning

confidence: 99%

CNN Based COVID-19 Prevention System

Shenvi

Shet

2021

2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)

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In order to effectively prevent the spread of COVID19 virus, almost everyone wears a mask during coronavirus epidemic. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train stations, etc. Therefore, it is very urgent to improve the recognition performance of the existing face recognition technology on the masked faces. Most current advanced face recognition approaches are designed based on deep learning, which depend on a large number of face samples. However, at present, there are no publicly available masked face recognition datasets. Compared to other datasets, Real-world Masked Face Recognition Dataset (RMFRD) is currently the world's largest real-world masked face dataset. Various COVID-19 prevention measures are undertaken such as wearing mask, sanitization, social distancing and temperature monitoring. An artificial intelligent IOT (Internet of Things) system with temperature monitoring, auto sanitization, mask detection is proposed. In this system, the machine is connected to a server by which the admin can monitor everything live from any place. The system also has face recognition feature by which the registered visitors, students can recognize separately and admin can maintain proper student database with temperature, auto sanitization system for door opening and closing system.

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Section: Literature Reviewmentioning

confidence: 99%

CNN Based COVID-19 Prevention System

Shenvi

Shet

2021

2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)

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“…Parametric methods provide simple estimates of future traffic conditions with low computational complexity. However, they are only applicable to specific traffic data conditions because changes in external conditions and the randomness and nonlinearity of traffic flow can impact prediction accuracy (Kong et al, 2020(Kong et al, , 2021.…”

Section: Traffic Flow Predictionmentioning

confidence: 99%

Coordinated ramp signal optimization framework based on time series flux-correlation analysis

Zhi¹,

Shu²,

Shen³

et al. 2021

PeerJ Computer Science

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Urban expressways provide an effective solution to traffic congestion, and ramp signal optimization can ensure the efficiency of expressway traffic. The existing methods are mainly based on the static spatial distance between mainline and ramp to achieve multi-ramp coordinated signal optimization, which lacks the consideration of the dynamic traffic flow and lead to the long time-lag, thus affecting the efficiency. This article develops a coordinated ramp signal optimization framework based on mainline traffic states. The main contribution was traffic flow-series flux-correlation analysis based on cross-correlation, and development of a novel multifactorial matric that combines flow-correlation to assign the excess demand for mainline traffic. Besides, we used the GRU neural network for traffic flow prediction to ensure real-time optimization. To obtain a more accurate correlation between ramps and congested sections, we used gray correlation analysis to determine the percentage of each factor. We used the Simulation of Urban Mobility simulation platform to evaluate the performance of the proposed method under different traffic demand conditions, and the experimental results show that the proposed method can reduce the density of mainline bottlenecks and improve the efficiency of mainline traffic.

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“…They introduce three use cases, specifically, remote robotic monitoring, smart medical data aggregation, and remote surgery, and discuss the implementation issues. Kong et al [16] propose an edge computing-based mask identification framework to enable precautions about the public epidemic, targeting for a use in buses with low-power camera devices. The framework consists of three parts, namely, video restoration, face detection, and mask identification.…”

Section: B Computing and Control Technologiesmentioning

confidence: 99%

Toward Agile Information and Communication Framework for the Post-COVID-19 Era

Peng

et al. 2021

IEEE Open J. Comput. Soc.

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In order to satisfy new normal life styles in the post-COVID-19 era, an efficient information and communication technology (ICT) framework is required to collect, process, and utilize data for the purpose of satisfying increasing user demand in communications and computing while providing a functionality of responding quickly to pandemic. We propose an ICT framework that is capable to support diverse application requirements based on ambient communication, resilient computing, and agile control technologies. Computer simulations are conducted to evaluate the fundamental functionalities of the proposed framework by using a case study where data are exchanged and processed among different network entities in unmanned aerial vehicle (UAV) empowered vehicular environments. The simulation results show that the proposed approach can outperform existing baselines in various conditions.

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Real-Time Mask Identification for COVID-19: An Edge-Computing-Based Deep Learning Framework

Cited by 91 publications

References 37 publications

CNN Based COVID-19 Prevention System

CNN Based COVID-19 Prevention System

Coordinated ramp signal optimization framework based on time series flux-correlation analysis

Toward Agile Information and Communication Framework for the Post-COVID-19 Era

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