用虚拟足球场生成数据，提升球场检测模型精度与泛化能力。
SoccerSynth Field: enhancing field detection with synthetic data from virtual soccer simulator
- 构建虚拟仿真生成的SoccerSynth-Field合成数据集用于预训练。
- 在真实数据集上测试，合成数据预训练模型表现更优。
- 适合需低成本、高鲁棒性球场检测的研究者与开发者。
团队运动中的场地检测是体育视频分析的关键任务。然而，收集大规模且多样化的现实世界数据集以训练检测模型通常成本高昂且耗时。合成数据集可通过控制光照、纹理和相机角度实现可变性，成为解决该问题的有前景替代方案。本研究通过探索使用合成数据集进行模型预训练的有效性，缓解真实数据采集的高成本与困难。本文提出并构建了名为SoccerSynth-Field的合成足球场数据集，用于预训练模型，并将其性能与基于真实数据集训练的模型进行对比。结果表明，经合成数据预训练的模型在足球场检测任务中表现更优，凸显合成数据在提升模型鲁棒性与准确性方面的有效性，为体育场地检测任务提供了一种成本低、可扩展的解决方案。
原文摘要 · Abstract (English)
Field detection in team sports is an essential task in sports video analysis. However, collecting large-scale and diverse real-world datasets for training detection models is often cost and time-consuming. Synthetic datasets, which allow controlled variability in lighting, textures, and camera angles, will be a promising alternative for addressing these problems. This study addresses the challenges of high costs and difficulties in collecting real-world datasets by investigating the effectiveness of pretraining models using synthetic datasets. In this paper, we propose the effectiveness of using a synthetic dataset (SoccerSynth-Field) for soccer field detection. A synthetic soccer field dataset was created to pretrain models, and the performance of these models was compared with models trained on real-world datasets. The results demonstrate that models pretrained on the synthetic dataset exhibit superior performance in detecting soccer fields. This highlights the effectiveness of synthetic data in enhancing model robustness and accuracy, offering a cost-effective and scalable solution for advancing detection tasks in sports field detection.
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