An Automated System to Limit COVID-19 Using Facial Mask Detection in Smart City Network

Rahman, Mohammad Marufur; Manik, Md. Motaleb Hossen; Islam, Md. Milon; Mahmud, Saifuddin; Kim, Jong-Hoon

doi:10.1109/iemtronics51293.2020.9216386

Cited by 151 publications

(51 citation statements)

References 19 publications

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“…Several works have investigated the possibility of using existing technologies to benefit infected patients. Rahman et al 35 proposed a deep-learning architecture to determine whether people are wearing a facial mask. The monitoring was realised via closed-circuit television cameras in public places.…”

Section: Literature Reviewmentioning

confidence: 99%

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients

Khozeimeh

Sharifrazi

Izadi

et al. 2021

Sci Rep

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COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.

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Section: Literature Reviewmentioning

confidence: 99%

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients

Khozeimeh

Sharifrazi

Izadi

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…A person tracking system with a three-part face mask recognition system, a person detector, a tracker, and a face mask classifier, was developed to facilitate face mask detection applications in smart cities [36]. Face mask classification or recognition, assuming faces were detected, has also been studied [19], [37], [38].…”

Section: B Face Mask Detectionmentioning

confidence: 99%

A Deep Learning Based Light-Weight Face Mask Detector With Residual Context Attention and Gaussian Heatmap to Fight Against COVID-19

2021

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Coronavirus disease 2019 has seriously affected the world. One major protective measure for individuals is to wear masks in public areas. Several regions applied a compulsory mask-wearing rule in public areas to prevent transmission of the virus. Few research studies have examined automatic face mask detection based on image analysis. In this paper, we propose a deep learning based single-shot light-weight face mask detector to meet the low computational requirements for embedded systems, as well as achieve high performance. To cope with the low feature extraction capability caused by the light-weight model, we propose two novel methods to enhance the model's feature extraction process. First, to extract rich context information and focus on crucial face mask related regions, we propose a novel residual context attention module. Second, to learn more discriminating features for faces with and without masks, we introduce a novel auxiliary task using synthesized Gaussian heatmap regression. Ablation studies show that these methods can considerably boost the feature extraction ability and thus increase the final detection performance. Comparison with other models shows that the proposed model achieves state-of-the-art results on two public datasets, the AIZOO and Moxa3K face mask datasets. In particular, compared with another light-weight you only look once version 3 tiny model, the mean average precision of our model is 1.7% higher on the AIZOO dataset, and 10.47% higher on the Moxa3K dataset. Therefore, the proposed model has a high potential to contribute to public health care and fight against the coronavirus disease 2019 pandemic.

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“…Technology is also being a good ally in surveillance and prevention tasks, such as the use of surveillance cameras to control the use of masks [44], infrared thermal cameras in airports to detect people with fever in real time [45] or drones to cover larger surveillance areas [46]. However, these conform a small representation of the whole set of applications where technology is being used, and this work pretends to provide another solution in this category: the combat of the pandemic.…”

Section: Related Workmentioning

confidence: 99%

COVID19-Routes: A Safe Pedestrian Navigation Service

et al. 2021

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COVID-19 has become a global pandemic during 2020 due to its high contagiousness and the high mobility of the world's population today. In just one year, this virus has caused millions of infections and deaths worldwide. These numbers will continue to grow until the population becomes immune to the virus thanks to an effective vaccine. Until this is possible, the only viable strategy is to try to stop its expansion through preventive measures such as limiting mobility, the use of masks, etc. In order to support these measures, this article presents a service to provide safe navigation solutions to reduce the likelihood of infection by avoiding potential conflict areas in the city. To identify these hotspots, a strategy that combines a rule-based system and a common-sense knowledge base is proposed. Through this strategy, an occupation model and a danger model are inferred. This requires the prior capture of knowledge about the general functioning of the city, its inhabitants and the virus. The proposed service makes decisions from these two models. Finally, a validation process has been carried out through surveys to evaluate the proposed solution. Obtained results demonstrate the potential of the proposed solution as a tool to identify safe routes that allow citizens to move around the city with low exposure to COVID-19.INDEX TERMS commonsense knowledge, covid-19, rule-based systems, smart city

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An Automated System to Limit COVID-19 Using Facial Mask Detection in Smart City Network

Cited by 151 publications

References 19 publications

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients

A Deep Learning Based Light-Weight Face Mask Detector With Residual Context Attention and Gaussian Heatmap to Fight Against COVID-19

COVID19-Routes: A Safe Pedestrian Navigation Service

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