Real-time Face Mask Detection System on Edge using Deep Learning and Hardware Accelerators
Stavan Ruparelia, Monil Jethva, Ruchi Gajjar · 2021
Real-time face mask detection with the use of Artificial Intelligence is one of the most advanced ways of detecting face masks and their wearing condition in public or private areas. In this work, a system based on Object Detection models is proposed which can detect and classify the type of mask wearing conditions in real-time. The system is implemented with two latest deep convolutional neural networks; YOLOv5s and YOLOv5l. The proposed system can efficiently detect and classify face masks based on their wearing condition as well as count them and store the count into a CSV file format with a timestamp. To perform real-time inference, the deep learning models were deployed on Nvidia Jetson Nano and Jetson Xavier NX which are embedded solutions inspired by Edge AI. The detection algorithms achieved mAP of 86.43 and 92.49 for YOLOv5s and YOLOv5l respectively. Comparing the mAP of both detection models, YOLOv5l achieved higher mAP than YOLOv5s while comparing fps on both hardware, Nvidia Jetson Xavier NX provides more fps than Nvidia Jestion Nano for realtime inference.