Attention-enhanced multiscale fusion for face mask-wearing detection

Abstract

Mask wearing is known as one of the most effective and convenient methods for preventing the transmission of viruses. Accurately determining whether a person is wearing a mask correctly is crucial in curbing the widespread transmission of viruses in public places. To address the limitations of traditional approaches, such as insufficient feature extraction and limited generalizability, an attention-enhanced multiscale fusion approach is proposed for assisting face mask-wearing detection. We first incorporate the simple, parameter-free attention module, adaptively spatial feature fusion module, and wise intersection over union loss function into a multiscale feature fusion framework, constructing the YSAW mask-wearing detection network, resulting in the development of the YSAW mask-wearing detection network. The network was then trained and evaluated on the WIDER face dataset and MAFA dataset, which includes faces, masks, and face masks. Experimental results demonstrate that the proposed YSAW network achieves mean average precision at intersection over union threshold 0.5 (mAP50 ¼ 94.3%), indicating superior detection performance in comparison to existing methods.