earticle

영어

Covid-19 has been substantially impacting all major sectors of life since its outbreak in the early 2020. Owing to the sheer contagiousness and rapid transmission, the World Health Organization (WHO) issued stringent precautionary measures such as wearing facemask and keeping social distance to curb the spread of the pandemic. To enforce these precautionary measures, governments and multifarious private sectors across the world leveraged Deep Learning (DL) especially Computer Vision (CV). In this regard, the CV research community has paid greater focus on social distancing and facemask detection tools. DL undoubtedly exhibits better performance on large amount of properly annotated data. Therefore, this work focuses on the development of a large-scale and diversified facemask detection dataset that contains images of faces with masks and without masks under different lightning conditions and varying angles. The remarkable training and testing performance achieved by YOLOv4 on real-life test videos and movies, attests the diversity of the dataset samples.