Design of lightweight mask wearing detection model based on YOLOv4

Affected by respiratory infectious diseases, wearing masks has become the norm in many public places. Using machine inspection instead of manual inspection can greatly save human resources. Commonly used general target detection models based on deep neural networks often have a huge number of parameters, which is not conducive to terminal deployment and cost control. In response to these problems, this paper proposes a lightweight mask detection model G-YOLOv4 based on the improved general target detection model YOLOv4. Using GhostNet to decrease the parameters and computation of the discovery model, the network also increase the accuracy of the model discovery. The use of depth separable convolution instead of the general convolution in the original YOLOv4 further reduces the computational complexity of the model. Use the Mish activation function as the activation function of the GhostNet shallow network to optimize the convergence effect of the model. The experimental results show that the mAP of the optimized G-YOLOv4 algorithm reaches 93.21 %, which has an increase rate of 5.52 % compared with the original algorithm. The model parameter size is 11488785, which has a reduction rate of 82.05 % compared with the original algorithm. The calculation amount of the model is 7.05 GFLOPs, which has a reduction rate of 72.8 % compared with the original algorithm. The detection efficiency (FPS) of the model reaches 38.23, which has an increase rate of 204.14 % compared with the original algorithm.