Application of Deep Learning Technology in Strength Training of Football Players and Field Line Detection of Football Robots - Zhou, Daliang; Chen, Gang; Xu, Fei (Frontiers in Neurorobotics, 2022)
The purpose of the research 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. Firstly, 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. Secondly, 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 time stream 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.