Implementing Anchor Free Model for Social Distancing Detection on FPGA Board
Riadh Ayachi
Universit of Monastir
Mouna Afif
Yahia said
Abdessalem Ben Abdelali
DOI: https://doi.org/10.59429/ifr.v2i1.6556
Keywords: COVID-19, social distance detection, Deep convolutional neural networks, Xilinx ZCU 102
Abstract
Since 2019, the world has known a global pandemic caused by the COVID-19 virus. The epidemic was spreading very fast and many precautions should be respected to fight the disease. In order to limit the CVID-19 spread, different ways can be adopted. Social distancing is one of the most important cautions that should be respected. It means keeping a safe social distance between persons in order to avoid COVID-19 contagion. The social distance is about one meter at least. Building new tools used to perform social distance system present a very challenging task.
We propose in this paper to build a social distancing system based on one-stage neural networks. The proposed system is developed based on an improved version FCOS model. In order to ensure an embedded implementation of the proposed work, we used EfficientNet v1 as a network backbone and we applied compression techniques to reduce the model complexity and computation resources. The inference stage of the model has been performed on a ZCU 102 board. Training and testing experiments have demonstrated the efficiency of the proposed work in terms of accuracy as well as processing time.
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