Research on Improved Mask Detection Method Based on YOLOv5 Algorithm

The existing mask detection models cannot balance detection accuracy and detection speed, and have a large number of parameters. In order to solve these problems, an improved YOLOv5 based mask detection algorithm is proposed. It mainly includes the following four improvements:Firstly, replace the C3 module in the YOLOv5s backbone network with the lightweight network GhostNetV2 to reduce the number of parameters; Secondly, replace the last C3 module of the YOLOv5s backbone extraction network and the C3 module of the last layer of Neck with a Swin Transformer structure to obtain more complete feature information and improve detection performance; Thirdly, introduce CBAM attention mechanism to better focus on key information, thereby improving detection efficiency and accuracy; Fourth, the loss function replaces GIoU with EIoU to improve positioning accuracy and speed up convergence. The experimental results on the AIZOO dataset show that the mAP value of the proposed impoved algorithm reaches 96.2%, Params is reduced to 6.6× 106, and FPS is as high as 136. There is also a good improvement on the validation dataset, and compared to other algorithms, the performance is better. The proposed improved algorithm is more suitable for mask detection. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.