A novel hybrid face mask detection approach using Transformer and convolutional neural network models

Face and face mask detection are one of the most popular topics in computer vision literature. Face mask detection refers to the detection of people's faces in digital images and determining whether they are wearing a face mask. It can be of great benefit in different domains by ensuring public safety through the monitoring of face masks. Current research details a range of proposed face mask detection models, but most of them are mainly based on convolutional neural network models. These models have some drawbacks, such as their not being robust enough for low quality images and their being unable to capture long-range dependencies. These shortcomings can be overcome using transformer neural networks. Transformer is a type of deep learning that is based on the self-attention mechanism, and its strong capabilities have attracted the attention of computer vision researchers who apply this advanced neural network architecture to visual data as it can handle long-range dependencies between input sequence elements. In this study, we developed an automatic hybrid face mask detection model that is a combination of a transformer neural network and a convolutional neural network models which can be used to detect and determine whether people are wearing face masks. The proposed hybrid model's performance was evaluated and compared to other state-of-the-art face mask detection models, and the experimental results proved the proposed model's ability to achieve a highest average precision of 89.4% with an execution time of 2.8 s. Thus, the proposed hybrid model is fit for a practical, real-time trial and can contribute towards public healthcare in terms of infectious disease control.