Qengineering / Face-Mask-Detection-Jetson-Nano

Face mask detection on a Jetson Nano
https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html
BSD 3-Clause "New" or "Revised" License
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aarch64 cpp deep-learning face-detection face-mask face-mask-detection face-recognition high-fps jetson-nano ncnn ncnn-framework paddle paddle-lite ssd-model

Face-Mask-Detection-Jetson-Nano

output image

A fast face mask recognition running at 44-5 FPS on a Jetson Nano.

License

This is a fast C++ implementation of two deep learning models found in the public domain.

The first is face detector of Linzaer running on a ncnn framework.
https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB.

The second is the Paddle Lite mask detection which classifies the found faces.
https://github.com/PaddlePaddle/Paddle-Lite/tree/develop/lite/demo/cxx/mask_detection.

The frame rate depends on the number of detected faces and can be calculated as follows:
FPS = 1.0/(0.022 + 0.008 x #Faces) when overclocked to 2014 MHz.

Paper: https://arxiv.org/abs/1905.00641.pdf
Size: 320x320

Special made for a Jetson Nano see Q-engineering deep learning examples

New version 2.0.

A new and superior version with only TensorFlow Lite for a Jetson Nano see GitHub

Dependencies.

April 4 2021: Adapted for ncnn version 20210322 or later

To run the application, you have to: