Research on the design technology of mobile Mix-YOLOv5s model

Abstract

Because the memory resources and computing resources of embedded devices are very limited, it is very difficult to deploy deep learning models on embedded devices. Therefore, how to efficiently and conveniently extract more feature maps from the limited feature maps in convolutional neural network has become a mainstream research direction. In the era of epidemic normalization, in order to deploy a mask face detection system more suitable for the actual scene in densely populated places such as communities, shopping malls, airports, railway stations and so on. This paper proposes the Mix-YOLOv5s model, which mainly combines the YOLOv5s model with the lightweight Ghost module, the CBAM attention module, Bidirectional Feature Pyramid Network(BiFPN), Mish activation function and Alpha-IoU loss function. They are used to promote the object detection ability of the model. We make quantitative and qualitative comparison between Mix-YOLOv5s and YOLOv5s. Compared with the YOLOv5s model, this model is able to improve the comprehensive performance by a small amount on the basis of greatly reducing Parameters and GFLOPs. Therefore, Mix-YOLOv5s model has great significance and advantages in the design of mobile terminal deep learning model, which can correctly judge whether the subjects wear masks, and has strong research value and broad application prospects.