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Social Distancing and Face Mask Detection using Deep Learning and Computer Vision

Shete, Isha (2020) Social Distancing and Face Mask Detection using Deep Learning and Computer Vision. Masters thesis, Dublin, National College of Ireland.

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Abstract

A novel virus has caused a world pandemic and huge life losses. Declared by the World Health Organization (WHO), this coronavirus originated from Wuhan, China in late December 2019. Upon thorough research, the virus has been observed as pathogenic and transmissible by air or by coming in close contact with an infected person. To avoid the spread of this virus, many measures have been suggested, such as maintaining a social distance, that is, maintaining a proper physical distance between people and lessening close contact with each other, and wearing a face mask to avoid the droplets from transmitting through the air. Therefore, this research paper focuses and aims its study towards implementing a Social Distancing and Face Mask Detection System. This system will implement object detection and facial recognition in the video footages of pedestrians. Pretrained models such as the YOLOv3, ResNet Classifier and DSFD are used. People violating the minimum distance were detected as well as faces without face-masks were detected. An overall results board is displayed in the output containing the number of people violating or non-violating the respective measures. After implementing and deploying the models, this research project achieved a confidence score of 100%. Therefore, this research project concludes with proven facts that social distancing and wearing face masks helps reduce the spread of the virus and thus builds a model to help detect these measures.
Keywords – COVID-19, Social Distancing, Pedestrian Detection, Face Mask Detection, ResNet Classifier.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 21 Jan 2021 10:55
Last Modified: 21 Jan 2021 10:55
URI: http://norma.ncirl.ie/id/eprint/4419

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