Selected article for: "CNN architecture and convolutional neural network"

Author: Balasa, R. I.; Olaru, G.; Bilu, C. M.; Balaceanu, M. B.
Title: A neural network based approach to identifying COVID-19 safety regulations compliance
  • Cord-id: u6unvpf8
  • Document date: 2021_1_1
  • ID: u6unvpf8
    Snippet: This paper proposes an image processing and neural network based automated solution for identifying whether or not people wear their masks properly and maintain the minimum required social distance. This system has two components: a subsystem which estimates the distance between two persons (which is performed via Haar cascades) and a subsystem which identifies whether or not a person wears a mask (which is performed via the MobileNetV2 convolutional neural network (CNN) architecture). © 2021 I
    Document: This paper proposes an image processing and neural network based automated solution for identifying whether or not people wear their masks properly and maintain the minimum required social distance. This system has two components: a subsystem which estimates the distance between two persons (which is performed via Haar cascades) and a subsystem which identifies whether or not a person wears a mask (which is performed via the MobileNetV2 convolutional neural network (CNN) architecture). © 2021 IEEE.

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