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

No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 36

AAAI Student Abstract and Poster Program

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.

10.1609/aaai.v36i11.21588