MOXA: A Deep Learning Based Unmanned Approach For Real-Time Monitoring of People Wearing Medical Masks

Abstract

With 6.93M confirmed cases of COVID-19 worldwide, making individuals aware of their sanitary health and ongoing pandemic remains the only way to prevent the spread of this virus. Wearing masks is an important step in this prevention. Hence, there is a need for monitoring if people are wearing masks or not. Closed circuit television (CCTV) cameras endowed with computer vision function by embedded systems, have become popular in a wide range of applications, and can be used in this case for real time monitoring of people wearing masks or not. In this paper, we propose to model this task of monitoring as a special case of object detection. However, real-time scene parsing through object detection running on edge devices is very challenging, due to limited memory and computing power of embedded devices. To deal with these challenges, we used a few popular object detection algorithms such as YOLOv3, YOLOv3Tiny, SSD and Faster R-CNN and evaluated them on Moxa3K benchmark dataset. The results obtained from these evaluations help us to determine methods that are more efficient, faster, and thus are more suitable for real-time object detection specialized for this task.

A Deep Learning Based Light-Weight Face Mask Detector With Residual Context Attention and Gaussian Heatmap to Fight Against COVID-19

“…Face mask detection algorithms have become more topical recently, since masks can help control the spread of COVID-19 during the pandemic. The algorithmic task focuses only on detecting physical masks, as shown in [18], [20], [21], [23], [34], [35]. Among these, YOLO based models are the most popular detectors.…”

“…A distance intersection over union non-maximum suppression (DIOU-NMS) algorithm was used to improve the post-processing stage of YOLOv3 [35]. YOLOv3 achieved the highest mAP in a comparison of YOLOv3, YOLOv3-tiny, SSD, and Faster R-CNN on the newly-established Moxa3K face mask detection dataset [23]. A person tracking system with a three-part face mask recognition system, a person detector, a tracker, and a face mask classifier, was developed to facilitate face mask detection applications in smart cities [36].…”

“…The Moxa3K face mask detection dataset is a public dataset to facilitate face mask research [23]. It contains 3,000 images with 2,800 for training and 200 for testing.…”

“…Faster R-CNN is the best regarded two-stage detector using an RPN [33]. YOLOv3 [49] and YOLOv3-tiny [49] are the most popular fast detectors used in the face mask literature [23], [34], [35]. YOLOv3 uses Darknet-53 as its backbone and three detection heads to process three-scale features enhanced by FPN.…”

“…Evaluations of this study were performed on two publicly available face mask datasets, the AIZOO [22] and Moxa3K [23] face mask datasets. Experimental results showed that the proposed model achieved state-of-the-art results on both datasets.…”