Players and ball detection are among the first required steps on a football analytics platform. Until recently, the existing open datasets on which the evaluations of most models were based, were not sufficient. In this work, we point out their weaknesses, and with the advent of the SoccerNet v3, we propose and deliver to the community an edited part of its dataset, in YOLO normalized annotation format for training and evaluation. The code of the methods and metrics are provided so that they can be used as a benchmark in future comparisons. The recent YOLO8n model proves better than FootAndBall in long-shot real-time detection of the ball and players on football fields.

翻译：球员和球检测是足球分析平台首要步骤之一。直到最近，多数模型评估所依赖的现有开放数据集仍存在不足。本研究指出这些数据集的局限性，并借助SoccerNet v3的发布，向社区提供并交付其数据集的改良版本——采用YOLO标准化注释格式，可用于训练与评估。相关方法与评估指标的代码已提供，可作为未来比较研究的基准。最新YOLO8n模型在足球场远距离实时球和球员检测方面优于FootAndBall模型。