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Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices

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BinaryCoP

Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices

XOHW Team Number: xohw21-142

High Performance (Multi-Gate/Camera) Low Power (Single Gate/Camera)

Our Goal

Deploy accurate, unbiased image classification algorithms, which can be used at entrances or speed-gates to check correct mask wear and positioning, with all processing on low-power, cheap, edge hardware to preserve privacy of passing users. Alt text

Example Notebook and Documentation

  • Example Jupyter notebook to test a prototype of BinaryCoP on a standard PYNQ-board.
  • Docs contains extended report and slides.
  • Other Utils:
    • Script to generate Grad-CAM interpretability results from trained networks.
    • DSP-BitPacking script to rewire XNOR operations through DSP blocks.

Requirements and Dependencies

  • PYNQ-Z1 with PYNQ 2.5 image.
  • DSP-BitPacking synthesis tested on Vivado 2018.1 with BNN-PYNQ.

Citation

This repository is based on the work published in IPDPS-RAW 2021.

@inproceedings{bcop,
author={Fasfous, Nael and Vemparala, Manoj-Rohit and Frickenstein, Alexander and Frickenstein, Lukas and Badawy, Mohamed and Stechele, Walter},
booktitle={2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
title={BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices},
year={2021},
pages={108-115},
doi={10.1109/IPDPSW52791.2021.00024}}

Acknowledgements:

@inproceedings{finn,
author = {Umuroglu, Yaman and Fraser, Nicholas J. and Gambardella, Giulio and Blott, Michaela and Leong, Philip and Jahre, Magnus and Vissers, Kees},
title = {FINN: A Framework for Fast, Scalable Binarized Neural Network Inference},
booktitle = {Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays},
series = {FPGA '17},
year = {2017},
pages = {65--74},
publisher = {ACM}}

@article{cabani.hammoudi.2020.maskedfacenet,
title={MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19},
author={Adnane Cabani and Karim Hammoudi and Halim Benhabiles and Mahmoud Melkemi},
journal={Smart Health},
year={2020},
url ={http://www.sciencedirect.com/science/article/pii/S2352648320300362},
issn={2352-6483},
doi ={https://doi.org/10.1016/j.smhl.2020.100144}}

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