Table tennis ball detection based on YOLOv5 and frameSORT

This paper proposes a frame-sorting module-FrameSORT and fuses it with YOLOv5 to create a novel visual detection and localization method, YOLO-FS, which can effectively and accurately detect table tennis ball moving targets, aiming at the omission and misdetection problems of YOLOv5 target detection in continuous image sequences as well as the poor robustness of traditional methods in detection. The Frame-SORT module uses the frame difference method and extended Kalman filtering for target detection and localization of frame images that are not identified by YOLO. The experimental results show that compared with other commonly used table tennis ball detection methods, this method achieves 94.6% detection accuracy in the task of video sequences, maintaining YOLO real-time detection while improving robustness, and has better detection accuracy and application prospects.