Lightweight Real-Time Mask Wearing Detection Algorithm Based on YOLOv3

The novel coronavirus has broken out from 2019 and quickly become a global pandemic. It spreads rapidly from person to person through droplets, aerosols and other carriers. In order to prevent the spread of the virus, people must wear masks when entering and leaving public places and taking public transport to reduce the risk of virus transmission. How to detect the wearing of masks in public places and other natural environments has become a new research problem. This paper proposes a lightweight deep neural network (E-YOLO) to realize mask wearing detection in real-time scenarios. E-YOLO improves the general target detection YOLOv3 algorithm by the follows methods. Firstly, the Efficient-Net series B2 backbone feature extraction network is used to replace the original Darknet53 feature extraction network. Combined with the spatial pyramid pooling module and the bidirectional feature pyramid structure to enrich the semantic information of the feature layer, so as to achieve the balance between the speed and accuracy of target detection in real-time scenes. The experimental results show that, compared with the YOLOv3 algorithm, the E-YOLO algorithm possesses the same accuracy and speed. The network floating point calculation amount is one twelfth of YOLOv3, and the model size is only one quarter of YOLOv3, which is more suitable for resource-constrained platforms.