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 transportations. However, if the entrances of some places are manually checked to see whether people are wearing a face mask or not, it becomes not only labor-intensive and time-consuming, but also inefficiently checking each passer-by. Therefore, this paper 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, a YOLOv5 model using the 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) records 94.42%. The average single-class running time is 0.016 seconds, and the file size of training model is 3.8MB. The recognition distance is up to 8m, and the maximum face rotation angle is 90°. In addition, a Petri net software tool, WoPeD, with graphical features based on mathematical theories, is used to verify the mask recognition system; and ensures the system has acceptable precision and recall values.