Mask Wearing Detection Algorithm Based on Improved SSD

ABSTRACT

In recent years, the COVID-19, which involves a highly infectious virus, has spread worldwide.Wearing masks in public areas can reduce the transmission and hence the spread of the virus.Additionally, using computer vision technology to detect mask wearing behavior in public areas is crucial.To prevent and control epidemics, the correct form of wearing face masks must be identified.In an actual environment, the detection of mask wearing is complex and diverse.The scale of a face wearing a mask is different;furthermore, the difference between the correct and wrong forms of wearing a mask is subtle and hence difficult to detect.Therefore, a mask wearing detection algorithm based on an improved Single Shot Multibox Detector(SSD) algorithm is proposed herein.Based on the SSD network, the algorithm introduces a feature fusion network and an attention coordination mechanism, reconstructs the feature extraction network, and enhances the ability of learning and processing detailed information.In addition, the classification prediction score and IoU score of the algorithm are combined, whereas the Quality Focal Loss(QFL) function is used to adjust the weight of positive and negative samples.An experiment is performed on acustom-developed mask wearing test dataset.Experimental results show that the average accuracy of the algorithm is 96.28%, which is 5.62% higher than that of the original algorithm.The improved algorithm offers good accuracy and practicability for mask wearing detection, as well assatisfies the requirements for epidemic prevention and control. © 2022, Editorial Office of Computer Engineering. All rights reserved.