A high-quality sport ball dataset annotation based on videos

In the task of object detection, detecting Sport Balls presents a
relatively challenging problem. This is primarily due to their small size
and lack of distinctive features, with the added difficulty of motion blur
caused by the high-speed movement of spherical objects in images. We have
observed that most data sources for Sport Ball detection tasks consist of
consecutive video frames from sports scenes. Building on this, we have
created a ball object dataset composed of table tennis, tennis, and soccer
videos (each containing over 10,000 target objects) to assist in training
models for detecting Sport Balls. Our dataset annotations follow the same
format as those used by ultralytics for object detection, defined as
bounding boxes specified by x, y, w, h. Experimental results presented in
the subsequent paper demonstrate that our dataset is of high quality, with
various models achieving strong performance on it.