Abstract
The COVID-19 pandemic has rendered social distancing and use of face masks as an absolute necessity today. Coming out of the epidemic, we're going to see this as the new normal and therefore most workplaces will require an identification system to permit employees based on the compliance of protocols. To ensure minimal contact and security, automatic entrance systems need to be employed in workplaces and institutions. For the implementation of such systems, we have investigated the performance of three object detection algorithms, namely SSD MobileNet V2, YOLO v3 and YOLO v4 in the context of real-time face mask detection. We conducted training and testing of these algorithms on our dataset focusing on various type of masks in the Indian community. We have exhibited in this paper that YOLOv4 transcends both YOLO v3 and SSD MobileNet V2 in sensitivity and precision and thus has a major use case in building AI identification systems.
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Gola, A., Panesar, S., Sharma, A., Ananthakrishnan, G., Singal, G., Mukhopadhyay, D. (2021). MaskNet: Detecting Different Kinds of Face Mask for Indian Ethnicity. In: Garg, D., Wong, K., Sarangapani, J., Gupta, S.K. (eds) Advanced Computing. IACC 2020. Communications in Computer and Information Science, vol 1367. Springer, Singapore. https://doi.org/10.1007/978-981-16-0401-0_39
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DOI: https://doi.org/10.1007/978-981-16-0401-0_39
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