Lightweight hand small target detection method based on improved YOLOv5s

In response to the issues of low frames per second and small gesture scale in current hand interaction detection leading to accuracy problems in hand detection, we propose a lightweight small target hand recognition method - RB-YOLOv5s. This method first solves the problem of excessive parameter quantity and low recognition accuracy by replacing the Conv module in YOLOv5s with the RepVGGBlock module and reducing the number of modules. Secondly, a bidirectional feature pyramid structure is introduced in the feature fusion network to enhance the degree of semantic information and location information fusion. Finally, the CIOU loss function in YOLOv5s is changed to SIOU to accelerate training speed and efficiency. We validated this method on a public dataset with distant small targets. The experimental results show that the recognition accuracy of our proposed model is 91%, the parameter quantity is only 1.754×10^6, and the Frames Per Second has increased to 104.17.