This study presents a complete pipeline for automated tennis match analysis. Our framework integrates multiple deep learning models to detect and track players and the tennis ball in real time, while also identifying court keypoints for spatial reference. Using YOLOv8 for player detection, a custom-trained YOLOv5 model for ball tracking, and a ResNet50-based architecture for court keypoint detection, our system provides detailed analytics including player movement patterns, ball speed, shot accuracy, and player reaction times. The experimental results demonstrate robust performance in varying court conditions and match scenarios. The model outputs an annotated video along with detailed performance metrics, enabling coaches, broadcasters, and players to gain actionable insights into the dynamics of the game.

翻译：本研究提出了一套完整的自动化网球比赛分析流程。我们的框架集成了多个深度学习模型，用于实时检测与追踪运动员及网球，同时识别用于空间参考的球场关键点。该系统采用YOLOv8进行运动员检测、定制训练的YOLOv5模型进行球体追踪，以及基于ResNet50架构的球场关键点检测，可提供包括运动员移动模式、球速、击球精度和运动员反应时间在内的详细分析数据。实验结果表明，该系统在不同球场条件和比赛场景下均表现出稳健的性能。模型输出带标注的视频及详细的性能指标，使教练、转播方和运动员能够从比赛动态中获得可操作的洞察。