IntroductionObject detection methods for team ball games players often struggle due to their reliance on dataset scale statistics, resulting in missed detections for players with smaller bounding boxes and reduced accuracy for larger bounding boxes.MethodsThis study introduces a two-fold approach to address these challenges. Firstly, a novel multi-scale attention mechanism is proposed, aiming to reduce reliance on scale statistics by utilizing a specially created SIoU (Similar to Intersection over Union) label that explicitly represents multi-scale features. This label guides the training of multi-scale attention network modules at two granularity levels. Secondly, an integrated scale equalization algorithm within SIoU labels enhances the detection ability of multi-scale targets in imbalanced samples.Results and discussionComparative experiments conducted on basketball, volleyball, and ice hockey datasets validate the proposed method. The relative optimal approach demonstrated improvements in the detection accuracy of players with smaller and larger scale bounding boxes by 11%, 7%, 15%, 8%, 9%, and 4%, respectively.

中文翻译：

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基于尺度注意力和尺度均衡算法的玩家检测方法

介绍团队球类比赛球员的目标检测方法通常由于依赖数据集规模统计而陷入困境，导致对较小边界框的球员的漏检，以及对较大边界框的准确度降低。方法本研究引入了一种两重方法来解决这些挑战。首先，提出了一种新颖的多尺度注意力机制，旨在通过利用专门创建的 SIoU（类似于并集交集）标签来明确表示多尺度特征，从而减少对尺度统计的依赖。该标签指导两个粒度级别的多尺度注意力网络模块的训练。其次，SIoU标签内的集成尺度均衡算法增强了不平衡样本中多尺度目标的检测能力。结果与讨论在篮球、排球和冰球数据集上进行的对比实验验证了所提出的方法。相对最优方法表明，具有较小和较大尺寸边界框的玩家的检测精度分别提高了 11%、7%、15%、8%、9% 和 4%。