A Deep Learning-Based Face Mask Detector for Autonomous Nano-Drones (Student Abstract)

Eiman AlNuaimi, Elia Cereda, Rafail Psiakis, Suresh Sugumar, Alessandro Giusti, Daniele Palossi

AAAI 2022 pp. 12903-12904

doi:10.1609/AAAI.V36I11.21588 /aaai/2022/alnuaimi2022aaai-deep/

Abstract

We present a deep neural network (DNN) for visually classifying whether a person is wearing a protective face mask. Our DNN can be deployed on a resource-limited, sub-10-cm nano-drone: this robotic platform is an ideal candidate to fly in human proximity and perform ubiquitous visual perception safely. This paper describes our pipeline, starting from the dataset collection; the selection and training of a full-precision (i.e., float32) DNN; a quantization phase (i.e., int8), enabling the DNN's deployment on a parallel ultra-low power (PULP) system-on-chip aboard our target nano-drone. Results demonstrate the efficacy of our pipeline with a mean area under the ROC curve score of 0.81, which drops by only ~2% when quantized to 8-bit for deployment.

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Cite

Text

AlNuaimi et al. "A Deep Learning-Based Face Mask Detector for Autonomous Nano-Drones (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21588

Markdown

[AlNuaimi et al. "A Deep Learning-Based Face Mask Detector for Autonomous Nano-Drones (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/alnuaimi2022aaai-deep/) doi:10.1609/AAAI.V36I11.21588

BibTeX

@inproceedings{alnuaimi2022aaai-deep,
  title     = {{A Deep Learning-Based Face Mask Detector for Autonomous Nano-Drones (Student Abstract)}},
  author    = {AlNuaimi, Eiman and Cereda, Elia and Psiakis, Rafail and Sugumar, Suresh and Giusti, Alessandro and Palossi, Daniele},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12903-12904},
  doi       = {10.1609/AAAI.V36I11.21588},
  url       = {https://mlanthology.org/aaai/2022/alnuaimi2022aaai-deep/}
}