关键词: 新型冠状病毒, 口罩佩戴检测, 特征金字塔网络, 自注意力机制, 损失函数

Abstract:

The 2019-nCoV can be transmitted through airborne droplets, aerosols, and other carriers. Correctly wearing a mask in public places can effectively prevent the infection of the virus. A face mask wearing detection method in a natural scene is proposed. The RetinaFace algorithm is improved to add the task of face mask wearing detection by optimizing the loss function. An improved self-attention mechanism is introduced into the feature pyramid network to enhance the expressive ability of the feature map. A data set containing 3, 000 pictures is created and manually annotated for network training. The experimental results show that the algorithm can effectively detect the wearing of masks, and has achieved good detection results in natural scene videos.

Key words: 2019-nCoV, mask wear detection, feature pyramid network, self-attention mechanism, loss function