ViDMASK dataset for face mask detection with social distance measurement

• Published in: Displays Vol. 73; p. 102235
• Main Authors: Ottakath, Najmath, Elharrouss, Omar, Almaadeed, Noor, Al-Maadeed, Somaya, Mohamed, Amr, Khattab, Tamer, Abualsaud, Khalid
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2022
• Subjects: Faster Mask RCNN with Resnet backbone and FPN; Mask detection; Mask Video dataset; Social distancing; YOLOR; YOLOV4; YOLOV4-tiny; YOLOV5; YOLOR; Mask detection; Mask Video dataset; YOLOV5; Faster Mask RCNN with Resnet backbone and FPN; Social distancing; YOLOV4-tiny; YOLOV4
• ISSN: 0141-9382; 1872-7387
• DOI: 10.1016/j.displa.2022.102235

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. With the existing video surveillance systems as base, a deep learning model is proposed for mask detection and social distance measurement. 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 on a newly proposed video mask detection dataset the ViDMASK. The obtained results achieved a comparatively high mean average precision of 92.4% for YOLOR. After mask detection, the distance between people’s faces is measured for high risk and low risk distance. Furthermore, the new large-scale mask dataset from videos named ViDMASK 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 most models are 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. •A Deep learning pipeline for mask detection and social distancing using state-of-art models.•Mask detection using YOLOv4, Fast RCNN with resnet101 and FPN, YOLOv4-tiny, YOLOv5 and YOLOR.•A very large challenging dataset created from more than 60 videos.•The dataset consists of 30,000 images of mask wearers and non-mask wearers in diverse environment.