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  • Special Issue Information Sciences

    Study on Automatic Detection of Dust Mask Wearing Status in Factories

    Wenyuan Jiang
    Yuhei Yamamoto
    Hajime Tachibana
    Keisuke Nakamoto
    Kunihiro Katai
    Naoya Nakagishi
    Hikaru Muranaka

    In construction and industrial work environments, workers are mandated to wear dust masks to ensure their health and safety. However, in actual field conditions, many workers neglect this requirement due to breathing discomfort and the heat and humidity within the factory. To address this issue, it is necessary to detect workers who are not wearing masks in real time and prompt them to put them on. Therefore, this study proposes a method to determine the wearing status of dust masks using deep learning based on video footage captured within the factory.

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