YOLOv8-G2F: A portable gesture recognition optimization algorithm

Abstract

Hand gesture recognition (HGR) is a significant research area with applications in human–computer interaction, artificial intelligence, and more. In the early stage of development of HGR, there are high hardware costs and large usage requirements. To reduce the high cost expenditure and increase the application scenario, deep learning has played a crucial role. With the greater depth perception and more computing power, currently HGR is more about continuous recognition in space based on vedio. But in this article, it considers that there is a growing demand for lightweight networks with high precision for end-to-end HGR applications. In that, it still tends to recognize consecutive video frames and get results quickly. This paper introduces an enhanced network called YOLOv8-G2F, which is based on YOLOv8. It incorporates improved lightweight modules not only replace the traditional convolution module of the network’s backbone and neck but also for the C2f module in YOLOv8. The network employs linear transformations, group convolution, and depthwise separable convolution to extract image information using simpler networks. Furthermore, model pruning is also used to further reduce model size and improve accuracy. The improved model achieved a recognition accuracy of 99.2% on the nus-ii gesture dataset with a model size of 2.33 MB. After extensive comparison and ablation experiments, YOLOv8-G2F demonstrated significant progress over existing algorithms.