## 4 Review of Mask-Face Detection Methodologies
### Two-Class Mask-Face Detection Methodologies
#### 4.1.2 Deep Learning Based Approaches  
In the field of machine learning, deep learning-based methods have achieved great popularity in recent years. Deep learning aims to build and simulate the neural network of the human brain for analysis and learning. It imitates the mechanism of the human brain to interpret data such as images, sounds, and texts. Many architectures have been proposed in object detection with the development of deep learning. As a branch of object detection, mask-face detection also includes the processes of target detection, feature extraction, classification, etc. According to the implementation of object detection methods, we divided mask-face detection models into the following two categories: two-stage and one-stage detection methods. In addition, there are some studies that only performed classification using convolutional neural networks and learned nothing about location information. Therefore, we divided deep learning methods into the following three parts: two-stage detection, one-stage detection and classification.  
A. Two-stage detectors  
Due to the lack of human and material resources and incomplete feature coverage in previous traditional machine learning models, two-stage detection generates a series of sample candidate boxes using the RPN [14] algorithm to obtain a more accurate detection effect. The main network includes the R-CNN series [13; 14; 51], etc.  
[PERSON] et al. [23] proposed a novel two-stage detector that also possessed the advantages of one-stage detectors. The model achieved a low inference time and high accuracy of 98.2%. In addition, adding affine transformation could more accurately locate faces in different poses so as to identify people who are not wearing masks and retrieve their identity information for registration. This method was helpful for shopping malls, schools, and other public places to check whether masks are worn through surveillance video.  
B. One-stage detectors  
Compared with the high accuracy of two-stage detectors, more research works tend to use the one-stage detector to detect and extract object features in pictures or videos with a higher detection efficiency, which could help achieve the target of real-time mask-face detection. One-stage detection methods mainly include the You Only Look Once (YOLO) series [15; 35; 52], Single Shot Detector (SSD) [33], etc. They directly returned the category probability and position coordinate values of objects through the model. Such an algorithm can provide faster detection, but the accuracy may be lower than that of the two-stage detection network.  
In the work of [PERSON] et al. [37], in order to solve the problem of the low accuracy of mask-wearing detection under dim light conditions, a method of introducing an attention mechanism into the YOLOv5 network for mask-wearing detection was proposed. By using CIOU Loss [53] to calculate the localization loss of the target frame, it not only considered the overlapping area of the predicted frame and the real frame but also accounted for the distance between the center points of the two and the aspect ratio of the two. At the same time, a convolutional block attention module (CBAM) [54] was added to the YOLOv5 network, which multiplied the input feature map and the attention map for adaptive feature optimization, highlighting the main features and suppressing irrelevant ones. The accuracy of this model reached 92% in the detection of mask-wearing under dim light conditions.