Optimization of Table Tennis Swing Action Supported by the Temporal Convolutional Network Algorithm in Deep Learning

To enhance the navigation accuracy and interpretability of Unmanned Aerial Vehicles (UAVs) in sports analysis, this study proposes an improved model based on the Temporal Convolutional Network (TCN) algorithm, integrated with Explainable Artificial Intelligence. The model aims to analyze real-time data collected by Internet of Things sensors mounted on UAVs to capture athletes’ swing actions during table tennis matches, thereby providing transparent decision support. The research effectively addresses the vanishing gradient problem by replacing the traditional Rectified Linear Unit (ReLU) activation function with Leaky ReLU, while simplifying the network structure through the use of a Global Average Pooling layer to reduce model complexity. Additionally, the residual structure is fine-tuned to adapt to dynamic environmental features, thereby enhancing the model’s capability to recognize swing actions. During the experimental phase, the model is evaluated using a dataset of swing actions collected by UAVs, comprising 55,582 data samples, which are divided into training and testing sets in a 3:2 ratio. The results reveal that the proposed improved TCN achieves remarkable outcomes in table tennis swing action recognition, with an algorithmic recognition accuracy of 99.43%, as well as recall, precision, and F1 scores of 99.00%. This algorithm’s recognition accuracy surpasses that of TCN, Long Short-Term Memory, and Convolutional Neural Network-Long Short-Term Memory algorithms by 10.57%, 3.65%, and 2.70%, respectively. The enhancement in recognition performance provides robust support for the technical training and competitive performance of table tennis players. This research outcome offers new insights into the application of UAVs in sports analysis and lays the groundwork for optimizing models and feature extraction techniques in broader sports application scenarios.