Face mask detection based on improved YOLOv3 algorithm

ABSTRACT

In the current situation of COVID-19 prevention and control, wearing masks remains an important way to prevent the transmission of the Novel Coronavirus. Aiming at the problem that the detection accuracy of the traditional YOLOv3 algorithm can still be improved, this paper proposes an improved yolov3 algorithm and applies it to the practical problem of detecting whether to wear a mask. Firstly, the algorithm introduces the residual structure of structural reparameterization in the feature extraction network named Darknet53 of YOLOv3 to obtain the input features;Secondly, the SimSPPF (Simplified Spatial Pyramid Pooling-Fast) is introduced to enhance feature extraction;Finally, an improved attention mechanism is introduced to make the model focus on regions with more prominent features. Besides, in order to ensure the accuracy of target detection, CIoU and Focal loss function was used in the training process. The results show that compared with the traditional YOLOv3, the detection accuracy of the improved algorithm for normal face and mask face is improved by 16.98% and 7.30% respectively, and the mAP is improved by 12.14%, which can meet the requirements of daily use and lay a foundation for rapid face recognition when wearing mask. () © 2023 IEEE.