  Wearing a mask is one of the important measures to prevent infectious
diseases. However, it is difficult to detect people's mask-wearing situation in
public places with high traffic flow. To address the above problem, this paper
proposes a mask-wearing face detection model based on YOLOv5l. Firstly,
Multi-Head Attentional Self-Convolution not only improves the convergence speed
of the model but also enhances the accuracy of the model detection. Secondly,
the introduction of Swin Transformer Block is able to extract more useful
feature information, enhance the detection ability of small targets, and
improve the overall accuracy of the model. Our designed I-CBAM module can
improve target detection accuracy. In addition, using enhanced feature fusion
enables the model to better adapt to object detection tasks of different
scales. In the experimentation on the MASK dataset, the results show that the
model proposed in this paper achieved a 1.1% improvement in mAP(0.5) and a 1.3%
improvement in mAP(0.5:0.95) compared to the YOLOv5l model. Our proposed method
significantly enhances the detection capability of mask-wearing.
