Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone
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TLDR
Wang et al. as discussed by the authors proposed a detection system based on the mobile phone, which extracted four features from the gray level co-occurrence matrixes (GLCMs) of the face mask micro-photos.Abstract:
Coronavirus Disease 2019 (COVID-19) has spread all over the world since it broke out massively in December 2019, which has caused a large loss to the whole world. Both the confirmed cases and death cases have reached a relatively frightening number. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of COVID-19, can be transmitted by small respiratory droplets. To curb its spread at the source, wearing masks is a convenient and effective measure. In most cases, people use face masks in a high-frequent but short-time way. Aimed at solving the problem that we do not know which service stage of the mask belongs to, we propose a detection system based on the mobile phone. We first extract four features from the gray level co-occurrence matrixes (GLCMs) of the face mask’s micro-photos. Next, a three-result detection system is accomplished by using K Nearest Neighbor (KNN) algorithm. The results of validation experiments show that our system can reach an accuracy of 82.87% (measured by macro-measures) on the testing dataset. The precision of Type I ‘normal use’ and the recall of type III ‘not recommended’ reach 92.00% and 92.59%. In future work, we plan to expand the detection objects to more mask types. This work demonstrates that the proposed mobile microscope system can be used as an assistant for face mask being used, which may play a positive role in fighting against COVID-19.read more
Citations
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A real time face mask detection system using convolutional neural network
TL;DR: In this article , a face mask detection model for static and real-time videos has been presented which classifies the images as "with mask" and "without mask". The model is trained and evaluated using the Kaggle data-set.
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Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm
S. Meivel,Nidhi Sindhwani,Rohit Anand,Digvijay Pandey,Abeer Ali Alnuaim,Alaa Saleh Altheneyan,Mohamed Yaseen Jabarulla,Mesfin Esayas Lelisho +7 more
TL;DR: A deep learning-enabled drone is designed for mask detection and social distance monitoring using Raspberry Pi 4 and a faster R-CNN algorithm.
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A Deep Learning Based Light-Weight Face Mask Detector With Residual Context Attention and Gaussian Heatmap to Fight Against COVID-19
Xinqi Fan,Mingjie Jiang,Hong Yan +2 more
TL;DR: Zhang et al. as mentioned in this paper proposed 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.
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Masked Face Recognition Using Deep Learning: A Review
TL;DR: In this paper, a survey of the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems is presented.
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Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning
TL;DR: In this paper, the authors used CNN with transfer learning to detect not only if a mask is used or not, but also other errors that are usually not taken into account but that may contribute to the virus spreading.
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