  Coronavirus 2019 has brought severe challenges to social stability and public
health worldwide. One effective way of curbing the epidemic is to require
people to wear masks in public places and monitor mask-wearing states by
utilizing suitable automatic detectors. However, existing deep learning based
models struggle to simultaneously achieve the requirements of both high
precision and real-time performance. To solve this problem, we propose an
improved lightweight face mask detector based on YOLOv5, which can achieve an
excellent balance of precision and speed. Firstly, a novel backbone
ShuffleCANet that combines ShuffleNetV2 network with Coordinate Attention
mechanism is proposed as the backbone. Afterwards, an efficient path aggression
network BiFPN is applied as the feature fusion neck. Furthermore, the
localization loss is replaced with alpha-CIoU in model training phase to obtain
higher-quality anchors. Some valuable strategies such as data augmentation,
adaptive image scaling, and anchor cluster operation are also utilized.
Experimental results on AIZOO face mask dataset show the superiority of the
proposed model. Compared with the original YOLOv5, the proposed model increases
the inference speed by 28.3% while still improving the precision by 0.58%. It
achieves the best mean average precision of 95.2% compared with other seven
existing models, which is 4.4% higher than the baseline.
