A Mask-Wearing Detection Model in Complex Scenarios Based on YOLOv7-CPCSDSA

Abstract

With the rapid development of deep learning technology, many algorithms for mask-wearing detection have achieved remarkable results. However, the detection effect still needs to be improved when dealing with mask-wearing in some complex scenes where the targets are too dense or partially occluded. This paper proposes a new mask-wearing detection model: YOLOv7-CPCSDSA. Based on YOLOv7, this model replaces some convolutions of the original model, CatConv, with FasterNet’s partial convolution (PConv) to form a CatPConv (CPC) structure, which can reduce computational redundancy and memory access. In the case of an increase in the network layer, the parameters are reduced instead. The Small Detection (SD) module is added to the model, which includes structures such as upsampling, concat convolution, and MaxPooling to enhance the ability to capture small targets, thereby improving detection accuracy. In addition, the Shuffle Attention (SA) mechanism is introduced, which enables the model to adaptively focus on important local information, thereby improving the accuracy of detecting mask-wearing. This paper uses comparative and ablation experiments in the mask dataset (including many images in complex scenarios) to verify the model’s effectiveness. The results show that the mean average precision@0.5 (mAP@0.5) of YOLOv7-CPCSDSA reaches 88.4%, which is 1.9% higher than that of YOLOv7, and its frames per second (FPS) rate reaches 75.8 f/s, meeting the real-time detection requirements. Therefore, YOLOv7-CPCSDSA is suitable for detecting mask-wearing in complex scenarios.