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.

翻译：佩戴口罩是预防传染病的重要措施之一。然而，在人员流动量大的公共场所，检测人们佩戴口罩的情况存在困难。针对上述问题，本文提出一种基于YOLOv5l的口罩佩戴人脸检测模型。首先，多头注意力自卷积不仅提高了模型的收敛速度，还增强了模型检测的准确性。其次，引入Swin Transformer Block能够提取更多有用特征信息，增强小目标检测能力，提升模型整体精度。我们设计的I-CBAM模块可提高目标检测精度。此外，采用增强特征融合能使模型更好地适应不同尺度的目标检测任务。在MASK数据集上的实验结果表明，与YOLOv5l模型相比，本文提出的模型在mAP(0.5)上提升了1.1%，在mAP(0.5:0.95)上提升了1.3%。本文方法显著增强了口罩佩戴检测能力。