State of art on object detection solution applied to COVID 19’s spreading prevention
Kawtar Retmi, Fatima Ouzayd, Hamid Ech-Cheikh, Chaymae Guarret, Youssef Taleb · 2021 IEEE International Conference on Data Science and Computer Application (ICDSCA) · 2021
In order to effectively prevent the spread of COVID19, people from different parts of the world were supposed to be wearing face masks after the WHO put it as a primordial instruction to stop its propagation. Researchers from different backgrounds gathered their efforts to ensure the respect of wearing face mask, namely AI field researchers. In this research, we are interested on the AI applications that were done from the beginning of the pandemic to prevent the COVID 19 contamination, especially those related to the mask wearing detection. The detection of wearing mask is classified as a computer vision problem, more specifically, an object detection one. Besides, with the evolution of the computational power and the availability of huge number of datasets, deep learning models using image and video processing techniques were proposed in order to detect people transgressing the wearing mask rule. In this paper we introduce a literature review of object detection, a case study of this problem which consists in the wearing mask detection, the related works as well as the different proposed solutions, and the suggested general pipeline for the treatment of this problem.