A Face-Mask Detection Approach based on YOLO Applied for a New Collected Dataset
Sahand Abbasi, Haniyeh Abdi, Ali Ahmadi
Abstract
Since the beginning of the COVID-19 pandemic, many lives are in danger. According to WHO (World Health Organization)'s statements, breathing without a mask is highly dangerous in public and crowded places. Indeed, wearing masks reduces the chance of being infected, and detecting unmasked people is a waste of resources if not performed automatically. AI techniques are used to increase the detection speed of masked and unmasked faces. In this research, a novel dataset and two different methods are proposed to detect masked and unmasked faces in real-time. In the first method, an object detection model is applied to find and classify masked and unmasked faces. In the second method, a YOLO face detector spots faces (whether masked or not), and then the faces are classified into masked and unmasked categories with a novel fast yet effective CNN architecture. By the methods proposed in this paper, the accuracy of 99.5% is achieved on the newly collected dataset.