Pei Baocheng, Sun Yanan, Fu Yebiao, Ren Ting
School of Physical Education, Jinjiang College, Sichuan University, Chengdu, Sichuan Province, People's Republic of China.
School of Physical Education, Nanning College for Vocational Technology, Nanning, Guangxi Province, People's Republic of China.
PLoS One. 2025 Jan 3;20(1):e0315130. doi: 10.1371/journal.pone.0315130. eCollection 2025.
In athletes' competitions and daily training, in order to further strengthen the athletes' sports level, it is usually necessary to analyze the athletes' sports actions at a specific moment, in which it is especially important to quickly and accurately identify the categories and positions of the athletes, sports equipment, field boundaries and other targets in the sports scene. However, the existing detection methods failed to achieve better detection results, and the analysis found that the reasons for this phenomenon mainly lie in the loss of temporal information, multi-targeting, target overlap, and coupling of regression and classification tasks, which makes it more difficult for these network models to adapt to the detection task in this scenario. Based on this, we propose for the first time a supervised object detection method for scenarios in the field of motion management. The main contributions of this method include: designing a TSM module that combines temporal offset operation and spatial convolution operation to enhance the network structure's ability to capture temporal information in the motion scene; designing a deformable attention mechanism that enhances the feature extraction capability of individual target actions in the motion scene; designing a decoupling structure that decouples the regression task from the classification task; and using the above approach for object detection in motion management scenarios. The accuracy of target detection in this scenario is greatly. To evaluate the effectiveness of our designed network and proposed methodology, we conduct experiments on open-source datasets. The final comparison experiment shows that our proposed method outperforms all the other seven common target detection networks on the same dataset with a map_0.5 score of 92.298%. In the ablation experiments, the reduction of each module reduces the accuracy of detection. The two types of experiments prove that the proposed method is effective and can achieve better results when applied to motion management detection scenarios.
在运动员的比赛和日常训练中，为了进一步提升运动员的运动水平，通常需要分析运动员在特定时刻的运动动作，其中快速、准确地识别运动场景中运动员、运动器材、场地边界等目标的类别和位置尤为重要。然而，现有的检测方法未能取得较好的检测结果，经分析发现，出现这种现象的原因主要在于时间信息丢失、多目标、目标重叠以及回归任务与分类任务的耦合，这使得这些网络模型更难适应此场景下的检测任务。基于此，我们首次提出一种针对运动管理领域场景的监督式目标检测方法。该方法的主要贡献包括：设计一个结合时间偏移操作和空间卷积操作的TSM模块，以增强网络结构在运动场景中捕捉时间信息的能力；设计一种可变形注意力机制，增强运动场景中单个目标动作的特征提取能力；设计一种将回归任务与分类任务解耦的解耦结构；并将上述方法应用于运动管理场景中的目标检测。此场景下目标检测的准确率大幅提高。为评估我们设计的网络和提出的方法的有效性，我们在开源数据集上进行实验。最终的对比实验表明，我们提出的方法在同一数据集上优于其他七个常见的目标检测网络，map_0.5分数达到92.298%。在消融实验中，每个模块的减少都会降低检测准确率。这两类实验证明了所提方法的有效性，并且在应用于运动管理检测场景时能够取得更好的效果。
