Design of enterprise worker safety detection algorithm based on YOLO

Abstract

Protecting the personal safety of on-site workers is an important task in enterprise production. In order to achieve widespread deployment to edge computing terminals, a lightweight object detection algorithm based on YOLOv5 is used to implement the personal safety detection task for workers. To achieve a lightweight task, PConv is utilized as the convolutional layer to decrease computational complexity, while Bi-Level Routing Attention is incorporated to enhance model accuracy. Furthermore, four detection heads are employed to improve object recognition capabilities. After experimentation, the precision can be improved by 3.4% compared with the baseline model, the parameters are reduced by 1.91MB, and the model size is decreased by 3.2MB.