An Embedded Machine Learning System For Real-time Face Mask Detection And Human Temperature Measurement

In this paper, an efficient embedded machine learning system is proposed to automatically detect face masks and measure human temperature in a real-time application. In particular, our system uses a Raspberry-Pi camera to collect realtime video and detect face masks by implementing a classification model on Raspberry Pi 3 in public places. The face mask detector is built based on MobileNetV2, with ImageNet pre-trained weights, to detect three cases of correctly wearing, incorrectly wearing and not wearing a mask. We also design a human temperature measurement framework by deploying a temperature sensor on the Raspberry Pi 3. The numerical results prove the practicality and effectiveness of our embedded systems compared to some state-of-the-art researches. The results of accuracy rate in detecting three cases of wearing a face mask are 98.61% based on the training results and 97.63% for validation results. Meanwhile, our proposed system needs a short time of 6 seconds for each person to be tested through the whole process of face mask detection and human forehead temperature measurement.