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ArXiv Preprint

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. Then we use BiFPN as the feature fusion neck. Furthermore, we replace the loss function of localization with -CIoU to obtain higher-quality anchors. Some valuable strategies such as data augmentation, adaptive image scaling, and anchor cluster operation are also utilized. Experimental results show the performance and effectiveness of the proposed model. On the basis of the original YOLOv5 model, our work increases the inference speed by 28.3% while still improving the precision by 0.58% on the AIZOO face mask dataset. It achieves a mean average precision of 95.2%, which is 4.4% higher than the baseline and is also more accurate compared with other existing models.

Sheng Xu