Abstract
This paper designs an epidemic prevention and control mask wearing detection system based on STM32, which is used to monitor the situation of people wearing masks. Tiny-YOLO detection algorithm is adopted in the system, combined with image recognition technology, and two kinds of image data with and without masks are used for network training. Then, the trained model can be used to carry out real-time automatic supervision on the wearing of masks in the surveillance video. When the wrong wearing or not wearing masks are detected, the buzzer will send an alarm, so as to effectively monitor the wearing of masks and remind relevant personnel to wear masks correctly.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Luoli, Yadav, A., Khan, A., Varish, N., Singh, P., Thakkar, H.K. (2024). Mask Wearing Detection System for Epidemic Control Based on STM32. In: Hassanien, A.E., Castillo, O., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. ICICC 2023. Lecture Notes in Networks and Systems, vol 731. Springer, Singapore. https://doi.org/10.1007/978-981-99-4071-4_56
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DOI: https://doi.org/10.1007/978-981-99-4071-4_56
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