A Novel IoT-Enabled System for Real-Time Face Mask Recognition Based on Petri Nets

Due to coronavirus disease 2019 (COVID-19), many countries have formulated pandemic prevention regulations, requiring the masses to wear a face mask before entering public places and taking public transportation. However, if the entrances of some places are manually controlled to check whether people are wearing a face mask or not, it becomes not only labor intensive but also time consuming. Therefore, this article aims to develop a face mask recognition system based on an edge computing platform. The traditional manual inspection control method is replaced by artificial intelligence (AI) technology to achieve automatic recognition and control. As an edge computing platform, Jetson Nano is an embedded system equipped with an AI platform, which can be used for object detection and image classification. Developed by Ultralytics LLC, the YOLOv5 model with PyTorch framework runs on the edge computing platform, featuring high speed, high precision, and small size. According to the model training results, the average precision (AP) reaches 95.41%, while the mean average precision (mAP) reaches 94.42%. The average single-class running time is 0.016 s, and the file size of the training model is 3.8 MB. The recognition distance is up to 8 m, and the maximum face rotation angle is 90°. In addition, a Petri net (PN) software tool, workflow Petri net designer (WoPeD), with graphical features based on mathematical theories, is used to verify the mask recognition system and ensures that the system has acceptable precision and recall values.