Abstract
The ongoing COVID-19 pandemic remains a significant threat, emphasizing the critical importance of mask-wearing to reduce infection risks. However, existing methods for mask detection encounter challenges such as identifying small targets and achieving high accuracy. In this paper, we present an enhanced YOLOv7 model tailored for mask-wearing detection. we employing a Generative Adversarial Network (GAN) to augment the original dataset, introducing the Convolutional Block Attention Module (CBAM) mechanism into the YOLOv7 model to enhance its small target detection capabilities, and replacing the model’s activation function with Parametric Rectified Linear Unit (FReLU) to improve overall performance. Experimental validation on a dataset showcases an average precision of 97.8% and a real-time inference speed of 64 frames per second (fps), meeting the real-time mask-wearing detection requirements effectively.
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