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.

基于YOLOv5l模型，本文提出了一种基于口罩佩戴的人脸检测模型。通过多头注意力自卷积和Swin Transformer Block的引入，提高了模型的检测准确性和收敛速度，并通过设计的I-CBAM模块和特征融合方法改善了不同尺度目标检测任务。实验证明，相比YOLOv5l模型，在MASK数据集上，本文模型的mAP(0.5)提升了1.1%，mAP(0.5:0.95)提升了1.3%。本文提出的方法显著提升了口罩佩戴的检测能力。

基于改进 YOLOv5 的口罩佩戴物体检测算法