ADEPTS: An Advanced Deep Ensemble and Progressively Training Strategy for High-speed Shuttlecock Localization

In recent times, integrating sports events with deep learning architectures has attracted significant attention, resulting in an increasing demand for applications in this field. In regards to sports such as shuttlecock or tennis, the precise monitoring of player and ball positions holds significant importance. This task is indispensable for a thorough understanding of the game's current state, serving both coaches and players. However, fast and unpredictable behavior makes extracting representative features a challenging issue that remains to be solved. To address this problem, we propose Advanced Deep Ensemble and Progressively Training Strategy (ADEPTS), which is an optimized training strategy designed for shuttlecock detection systems. ADEPTS combines multi-scale feature fusion and the progressive learning approach, allowing networks to capture the trajectory features of high-speed ball movement more accurately while improving training efficiency. Experimental results show that ADEPTS can significantly reduce training time by about 26.67% with high-resolution outputs. Additionally, it makes the network achieve even better localization accuracy, which makes it a practical and effective solution for real-world applications.