Research on the Application of Deep Learning in Ball Games

Abstract

Deep learning algorithms are widely used in image detection. Ball games, popular in sports, are amenable to data fiction due to their straightforward interaction models. With AI advancements, deep learning (DL) techniques like AlexNet, SSD, and DNN are increasingly applied in ball sports. These models have excelled in image and speech recognition, and their adaptation to ball sports research has led to advancements in ball classification/localization, trajectory tracking, game video analysis, and decision-making in multiplayer ball games. Deep learning models are able to learn complex features and patterns to detect and locate balls more accurately in images. This is very critical for ball detection in sports games, monitoring systems, and other fields, which can improve the accuracy and reliability of detection. Deep learning models can apply knowledge learned from one task to a related task through transfer learning. This makes it easier for the model to adapt to different types of ball detection problems, even with limited training data. The future of DL in ball sports is bright, promising significant changes and improvements in training, competition, and spectatorship.