Improving Ball Detection in Volleyball Using Deep Learning

In recent years, the application of deep learning techniques has revolutionized various domains, including the realm of sports analytics. The analysis of ball tracking and trajectory in sports has become an increasingly vital area of research, driven by advancements in technology and the growing demand for data-driven insights in athletic performance. In volleyball, a sport characterized by rapid movements and strategic play, the ability to accurately track the trajectory of the ball is crucial for both training and competitive analysis. This paper proposes novel deep learning models for accurate volleyball ball detection and tracking. By incorporating attention mechanisms into the YOLOv۸ and YOLOv۱۰ architecture, our models significantly improve performance, particularly in challenging situations involving occlusions and fast movements. The proposed models across several metrics compared to baseline and other models. Specifically, achieved precision (۹۴.۲% and ۹۴.۷%, respectively) and recall (۸۸.۱% and ۸۷.۶%, respectively) and real-time processing speeds, making them suitable for various sports analytics applications.

کلیدواژه ها:

Volleyball detection ، deep learning ، Convolutional neural network ، Attention mechanism