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      COVID Vision: An integrated face mask detector and social distancing tracker

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          Abstract

          The effects of the global pandemic are wide spreading. Many sectors like tourism and recreation have been temporarily suspended, but sectors like construction, development and maintenance have not been halted due to their importance to society. Such projects involve people working together in close proximity, thus leaving them susceptible to infection. It is recommended that people maintain social distance and wear a face mask to reduce the spread of COVID-19. To this effect, we propose COVID Vision - a system consisting of convolutional neural networks (CNNs) for a face mask detector, a social distancing tracker and a face recognition model to help people rely less on personnel and maintain the COVID-19 norms and restrictions. COVID Vision is able to detect, with great accuracy, if a person is wearing a mask or just covering their mouth with their hands as well as people's social distancing infractions from a live video in real time. It can also maintain a database of people who have tested positive for COVID-19 or are at risk using facial recognition.

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          Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.

          Kaiming He, Xiangyu Zhang, Shaoqing Ren … (2015)
          Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 × faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.
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            Is Open Access

            COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images

            Linda. Wang, Zhong Qiu Lin, Alexander Wong (2020)
            The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors’ knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors’ knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.
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              Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection

              Mohamed Loey, Gunasekaran Manogaran, Mohamed Taha … (2020)
              Highlights • A novel deep learning model for medical face mask detection. • The model can help governments to prevent the COVID-19 transmission. • Two medical face mask datasets have been tested. • The YOLO-v2 with ResNet-50 model achieves high average precision.
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                Author and article information

                Journal
                Title: International Journal of Cognitive Computing in Engineering
                Publisher: The Authors. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
                ISSN (Print): 2666-3074
                ISSN (Electronic): 2666-3074
                Publication date PMC-release: 13 May 2022
                Publication date (Electronic): 13 May 2022
                Affiliations
                [0001]School of Computer Science and Engineering, VIT University, Vellore, India
                Author notes
                [* ]Corresponding author.
                Article
                Publisher Item ID: S2666-3074(22)00011-0
                DOI: 10.1016/j.ijcce.2022.05.001
                PMC ID: 9098571
                SO-VID: 7a811b87-63a7-44ba-ad66-1a194b802136
                Copyright © © 2022 The Authors. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 12 March 2022
                Date revision received : 30 April 2022
                Date accepted : 10 May 2022
                Categories
                Subject: Article

                Keywords: computer vision convolutional neural networks haar cascade classifier,keras,machine learning tensorflow
                Data availability:
                Keywords: computer vision convolutional neural networks haar cascade classifier, keras, machine learning tensorflow

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