YOLOv10-based Model for Player and Football Detection

Abstract

This study presents an advanced YOLOv10n-based method for the automatic detection of football players and balls directly from match videos. We enhance the YOLOv10 architecture with several significant improvements, including additional detection heads, the integration of C2f_faster and C3_faster modules for enhanced processing speed and accuracy, and the inclusion of BotNet modules with self-attention mechanisms for managing complex visual scenes. Further, we incorporate GhostConv modules to reduce computational overhead while maintaining effective feature extraction. These architectural modifications ensure robust detection capabilities in real-time sports environments, addressing challenges such as high-speed movements, frequent occlusions, and variable lighting conditions typical of both indoor and outdoor stadiums. Validation on internet-sourced images from football matches demonstrates the practicality and effectiveness of our model.