Inheritance and Innovation Development of Sports Based on Deep Learning and Artificial Intelligence

Abstract

This work aims to speed up the intelligent construction of Sports and Leisure Characteristic Towns (SLCT) in the new era. AI and robot technologies have become the pillar of scientific research. First, the MobileNetV2 is simplified and improved using robot technology to design a lightweight Image Recognition (IR) system. The proposed IR system integrates the framework of Facial Recognition (FR) and Automatic License Plate Recognition (ALPR). Then, a hardware acceleration scheme for Convolution Neural Network (CNN) is designed to accelerate the calculation of the improved MobileNetV2-based IR system. Finally, an experiment is designed to verify the performance of the proposed IR system on FR and ALPR tasks. The results are summarized as follows: 1) FR performance, the proposed IR system achieves 99.46% and 99.23% recognition accuracy for CelebFaces and CASIA-Web face data sets on the Xilinx VC707 platform, respectively. Thus, it can recognize facial images efficiently and timely on devices without Graphics Processing Unit (GPU); 2) regarding ALPR performance, the proposed IR system uses NVIDIA Titan X GPU to process 960×640-pixel images with 30/Frames Per Second (FPS). Hence, it meets the requirements of real-time processing; (3) proposed hardware acceleration scheme is tested on LeNet to generalize the acceleration effect. Consequently, the proposed convolution acceleration scheme can lend to common CNN. Compared with Eyeriss, the performance of the LeNet convolution layer is significantly improved in efficiency and resource consumption. Therefore, the proposed acceleration scheme is excellent and feasible. In conclusion, the lightweight neural network can obtain high accuracy and good generalization. The proposed hardware acceleration scheme can accelerate CNN operation. The findings lay the foundation for developing robot technology and constructing the intelligent SLCT. It contributes to the inheritance and innovative development of intelligent towns.