用游戏引擎生成的合成数据提升盲人导航机器人的视觉模型性能。
Synthetic data augmentation for robotic mobility aids to support blind and low vision people
- 用Unreal Engine 4生成合成数据训练视觉模型
- 合成数据使多个导航任务性能提升
- 适合做无障碍辅助技术研究的开发者参考
面向盲人和低视力（BLV）人群的机器人导航辅助设备高度依赖基于深度学习的视觉模型，但其性能常受限于真实世界数据集的数量与多样性，而这些数据在不同任务中难以大规模收集。本研究探讨了使用Unreal Engine 4生成的合成数据在训练该安全关键应用中的视觉模型时的有效性。结果表明，合成数据能在多个任务上提升模型表现，展现出其潜力与局限性。研究为优化合成数据生成提供了宝贵见解，并公开发布了所生成的合成数据集，以支持盲人辅助技术领域的持续研究，数据集地址：https://hchlhwang.github.io/SToP。
原文摘要 · Abstract (English)
Robotic mobility aids for blind and low-vision (BLV) individuals rely heavily on deep learning-based vision models specialized for various navigational tasks. However, the performance of these models is often constrained by the availability and diversity of real-world datasets, which are challenging to collect in sufficient quantities for different tasks. In this study, we investigate the effectiveness of synthetic data, generated using Unreal Engine 4, for training robust vision models for this safety-critical application. Our findings demonstrate that synthetic data can enhance model performance across multiple tasks, showcasing both its potential and its limitations when compared to real-world data. We offer valuable insights into optimizing synthetic data generation for developing robotic mobility aids. Additionally, we publicly release our generated synthetic dataset to support ongoing research in assistive technologies for BLV individuals, available at https://hchlhwang.github.io/SToP.
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