Convolutional neural network-based intelligent AR teaching system for educational robots and human-computer interaction
In this paper,the intelligent AR teaching system of educational robots based on convolutional neural networks and hu-man-computer interaction are studied,and a hand pose estimation method based on improved HRNet network is proposed to improve the speed and accuracy of the intelligent AR teaching system of robots to recognize user gestures.Firstly,on the basis of the commonly used human-computer interaction process,the overall framework of the method for improving the accuracy of instruction recognition of robot system in this paper is designed,and then improved according to the shortcomings of HRNet network,that is,the Ghost module is used to replace the traditional convolution in the structure of HRNet network,which solves the problem of large computational cost of HRNet network.At the same time,the ECA-Net module is integrated into the residual structure of HRNet network,which further enhances the learning ability of the network model to learn the hand pose feature information.The experimental results show that the hand pose estimation method based on the improved HRNet network proposed in this paper is feasible and effective,and can quickly and accurately extract feature information and complete the hand pose estimation task,which provides a more efficient human-com-puter interaction technology for the intelligent AR teaching system of educational robots.
educational robotsintelligent AR teaching systemhuman-computer interactionHRNet Networkhand pose esti-mation
万方数据
维普
