Zhou Daliang, Chen Gang, Xu Fei
School of PE, Nanjing Xiaozhuang University, Nanjing, China.
School of Physical Education, Hangzhou Normal University, Hangzhou, China.
Front Neurorobot. 2022 Jun 29;16:867028. doi: 10.3389/fnbot.2022.867028. eCollection 2022.
The purpose of the study is to improve the performance of intelligent football training. Based on deep learning (DL), the training of football players and detection of football robots are analyzed. First, the research status of the training of football players and football robots is introduced, and the basic structure of the neuron model and convolutional neural network (CNN) and the mainstream framework of DL are mainly expounded. Second, combined with the spatial stream network, a CNN-based action recognition system is constructed in the context of artificial intelligence (AI). Finally, by the football robot, a field line detection model based on a fully convolutional network (FCN) is proposed, and the effective applicability of the system is evaluated. The results demonstrate that the recognition effect of the dual-stream network is the best, reaching 92.8%. The recognition rate of the timestream network is lower than that of the dual-stream network, and the maximum recognition rate is 88%. The spatial stream network has the lowest recognition rate of 86.5%. The processing power of the four different algorithms on the dataset is stronger than that of the ordinary video set. The recognition rate of the time-segmented dual-stream fusion network is the highest, which is second only to the designed network. The recognition rate of the basic dual-stream network is 88.6%, and the recognition rate of the 3D CNN is the lowest, which is 86.2%. Under the intelligent training system, the recognition accuracy rates of jumping, kicking, grabbing, and starting actions range to 97.6, 94.5, 92.5, and 89.8% respectively, which are slightly lower than other actions. The recognition accuracy rate of passing action is 91.3%, and the maximum upgrade rate of intelligent training is 25.7%. The pixel accuracy of the improved field line detection of the model and the mean intersection over union (MIoU) are both improved by 5%. Intelligent training systems and the field line detection of football robots are more feasible. The research provides a reference for the development of AI in the field of sports training.
本研究的目的是提高智能足球训练的性能。基于深度学习（DL），对足球运动员的训练和足球机器人的检测进行了分析。首先，介绍了足球运动员训练和足球机器人的研究现状，主要阐述了神经元模型和卷积神经网络（CNN）的基本结构以及DL的主流框架。其次，结合空间流网络，在人工智能（AI）背景下构建了基于CNN的动作识别系统。最后，通过足球机器人，提出了一种基于全卷积网络（FCN）的场地线检测模型，并评估了该系统的有效适用性。结果表明，双流网络的识别效果最佳，达到92.8%。时间流网络的识别率低于双流网络，最高识别率为88%。空间流网络的识别率最低，为86.5%。四种不同算法在数据集上的处理能力比普通视频集更强。时分双流融合网络的识别率最高，仅次于设计的网络。基本双流网络的识别率为88.6%，3D CNN的识别率最低，为86.2%。在智能训练系统下，跳跃、踢、抓和起跑动作的识别准确率分别为97.6%、94.5%、92.5%和89.8%，略低于其他动作。传球动作的识别准确率为91.3%，智能训练的最大升级率为25.7%。模型改进后的场地线检测的像素准确率和平均交并比（MIoU）均提高了5%。智能训练系统和足球机器人的场地线检测更可行。该研究为体育训练领域的人工智能发展提供了参考。
