Tennis Robot Design via Internet of Things and Deep Learning

Abstract

To efficiently integrate deep learning (DL) and Internet of Things (IoT) with tennis robot research, the concept and characteristics of mask region-convolutional neural network (Mask R-CNN) under DL are analyzed. Then, the IoT radio frequency identification (RFID) is introduced and the suitable RFID structure is established. Moreover, the real-time intelligent image recognition tennis robot is designed, and simulation experiment on the robot is performed. The results show that when the positioning label is eight, the convergence speed of the optimized algorithm is improved relative to the unoptimized one, and the error is reduced by 0.82. The positioning algorithm proposed in this research has high convergence speed and small system error. The positioning accuracy of the tennis robot is improved by at least 6%, the positioning targets are close to the target to be located, and the tennis robot has the best shortest path effect. In addition, the tennis recognition algorithm based on Mask R-CNN can accurately distinguish dense tennis balls with high accuracy. It shows that the tennis robot positioning algorithm and target recognition algorithm proposed in this research are of practical adoption value.

Keywords

• Deep learning
• Internet of Things
• Mask R-CNN
• RFID
• smart image
• tennis robot