LAUSR

Since the new crown epidemic, mask‐wearing has become a new normal in people's work and life. The inspection mechanism for mask‐wearing at the entrance and exit of public places is seriously insufficient. The phenomenon of “pick‐up on entry” has led to the severe formalization of mask‐wearing inspection. Manual detection of mask‐wearing in an open and dynamic crowded environment is unrealistic, which is not only time‐consuming and labor‐intensive but also cannot achieve early warning throughout the entire process. In response to this problem, this paper proposes a real‐time recognition and early warning method for mask‐wearing in an open, dynamic, complex environment based on improved YOLOv5 R6.1. First, replacing the first Conv structure of the backbone network in the YOLOv5 R6.1 model with an improved Stem structure to minimize the computational overhead while improving the performance. Then by normalizing the data, the random erasure data expansion technique is used to enhance the antiocclusion robustness of the algorithm. Finally, according to the mask‐wearing specification in the training data set, optimizing and adjusting the anchor box parameters of the YOLOv5 R6.1 model to improve the model's ability to recognize small targets. The experiments are based on open data sets, and the results show that the mean precision (mAP), precision, and recall of this method reach 92.9%, 94.1%, and 88.5% on average, and the average frames per second (FPS) reaches 117. Moreover, the mAP and FPS are improved by an average of 6.5% and 474% compared with algorithms based on RetinaNet, Attention‐Retina, Single Shot multibox Detector, Fast‐RCNN, YOLOv4, and YOLOv5.