ViDMASK dataset for face mask detection with social distance measurement.

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Abstract

The COVID-19 outbreak has extenuated the need for a monitoring system that can monitor face mask adherence and social distancing with the use of AI. Taking advantage of the existing video surveillance system, a deep learning based method for mask detection and social distancing is proposed. State-of-the-art object detection and recognition models such as Mask RCNN, YOLOv4, YOLOv5, and YOLOR were trained for mask detection and evaluated on the existing datasets and a proposed video mask detection dataset. The obtained results achieved a comparatively high mean average precision. After mask detection, the distance between people’s faces is measured. Furthermore, a new large-scale mask dataset from videos named VIDMASK is introduced. This diversifies the subjects in terms of pose, environment, quality of image, and versatile subject’ characteristics, producing a challenging dataset. The tested models succeed in detecting the face masks with high performance on the existing dataset, MOXA. However, with the VIDMASK dataset, the performance of these models is less accurate because of the complexity of the dataset and the number of people in each scene. The link to ViDMask dataset and the base codes are available at https://github.com/ViDMask/VidMask-code.git.© 2022 The Author(s).