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The rapid development of computer vision makes human-computer interaction possible and has a wide application prospect. Since the discovery of the first case of COVID-19, the global fight against the epidemic has begun. In addition to various studies and findings by medical and health care experts, people's daily behaviors have also become key to combating the epidemic. In China, the government has taken active and effective measures of isolation and closure, as well as the active cooperation of the general public, such as it is unnecessary to stay indoors and wear masks. China, as the country with the first outbreak of the epidemic, has now become the benchmark country of epidemic prevention in the world. Of course, it is not enough for people to wear masks consciously. Wearing masks in all kinds of public places still needs supervision. In this process, this paper proposes to replace manual inspection with a deep learning method and use YOLOV5, the most powerful objection detection algorithm at present, to better apply it in the actual environment, especially in the supervision of wearing masks in public places. The experimental results show that the algorithm proposed in this paper can effectively recognize face masks and realize the effective monitoring of personnel.
Yang et al. (Fri,) studied this question.