Abstract
Effective strategies to control COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy, with the brim-full horizon yet to unfold. In the absence of effective antiviral and limited medical resources, many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources. Wearing mask is among the non-pharmaceutical intervention measures that can be used as barrier to primary route of SARS-CoV2 droplets expelled by presymptomatic or asymptomatic individuals. Regardless of discourse on medical resources and diversities in masks, all countries are mandating coverings over nose and mouth in public areas. Towards contribution of public health, the aim of the paper is to devise a real-time technique that can efficiently detect non mask faces in public and thus enforce to wear mask. The proposed technique is ensemble of one stage and two stage detectors to achieve low inference time and high accuracy. We took ResNet50 as a baseline model and applied the concept of transfer learning to fuse high level semantic information in multiple feature maps. In addition, we also propose a bounding box transformation to improve localization performance during mask detection. The experiments are conducted with three popular baseline models namely ResNet50, AlexNet and MobileNet. We explored the possibility of these models to plug-in with the proposed model, so that highly accurate results can be achieved in less inference time. It is observed that the proposed technique can achieve high accuracy (98.2%) when implemented with ResNet50. Besides, the proposed model can generate 11.07% and 6.44% higher precision and recall respectively in mask detection when compared to RetinaFaceMask detector.
Highlights
The 209th report of world health organization (WHO) published on August 16, 2020 reported that coronavirus disease (COVID-19) caused by acute respiratory syndrome (SARS-CoV2) has globally infected more than 6 million people and caused over 379,941 deaths worldwide [1]
CMES, 2021, vol.127, no.2 pandemic carried by researchers at University of Edinburgh reveals that wearing face mask or other covering over nose and mouth cuts risk of Coronavirus spread by avoiding forward distance travelled by person’s exhaled breath by more than 90% [3]
The newly created dataset, feature engineering through transfer learning of robust ResNet50 pre-trained model, optimal face detection approach with improved localization using affine transformation and avoidance of overfitting resulted in an overall system that can be installed in thermal cameras at public places to curtail spread of Coronavirus
Summary
The 209th report of world health organization (WHO) published on August 16, 2020 reported that coronavirus disease (COVID-19) caused by acute respiratory syndrome (SARS-CoV2) has globally infected more than 6 million people and caused over 379,941 deaths worldwide [1]. Steffen et al carried an exhaustive study to compute the community-wide impact of mask use in general public, a portion of which may be asymptomatically infectious in New York and Washington Their works show that near universal adoption (80%) of even weak mask (20% effective) could prevent 17%–45% of projected deaths over two months in New Work and reduces the peak daily death rate by 34%–58% [4,5]. Their results strongly recommend use of face mask in general public to curtail spread of Coronavirus. To mandate the use of facemask, it becomes essential to devise some techniques that enforce individual to apply mask before exposure to public places
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