构建首个融合真实与合成图像的触觉地砖数据集，助力视障者安全导航
GuideTWSI: A Diverse Tactile Walking Surface Indicator Dataset from Synthetic and Real-World Images for Blind and Low-Vision Navigation
- 融合合成与真实图像，覆盖条形与圆顶两种触觉地砖
- 包含10,000+张带标注图像，支持多视角训练
- 填补北美欧洲常见圆顶地砖数据空白，适配全球导航系统
触觉步行表面指示器（TWSIs）是视障和低视力（BLV）行人用于识别路口与危险区域的关键安全地标。通过与视障导盲犬使用者、培训师及方位与移动专家的观察会话，我们确认了高精度TWSI分割对导航辅助至关重要。实现这一目标需要大规模标注数据。然而，现有城市感知数据集中TWSI严重缺失，即便专用铺装数据集也存在局限：缺乏机器人相关视角（如第一人称或俯视），且地理分布偏倚于东亚常见的方向条纹——即沿人行道连续引导的平行凸起条。这种聚焦忽略了北美洲和欧洲广泛使用的截断圆顶——成排圆形凸起，用于提醒路边、路口及站台边缘。因此，仅基于条纹数据训练的模型难以泛化到圆顶类警告，导致在关键环境中漏检或误停。
原文摘要 · Abstract (English)
Tactile Walking Surface Indicators (TWSIs) are safety-critical landmarks that blind and low-vision (BLV) pedestrians use to locate crossings and hazard zones. From our observation sessions with BLV guide dog handlers, trainers, and an O&M specialist, we confirmed the critical importance of reliable and accurate TWSI segmentation for navigation assistance of BLV individuals. Achieving such reliability requires large-scale annotated data. However, TWSIs are severely underrepresented in existing urban perception datasets, and even existing dedicated paving datasets are limited: they lack robot-relevant viewpoints (e.g., egocentric or top-down) and are geographically biased toward East Asian directional bars - raised parallel strips used for continuous guidance along sidewalks. This narrow focus overlooks truncated domes - rows of round bumps used primarily in North America and Europe as detectable warnings at curbs, crossings, and platform edges. As a result, models trained only on bar-centric data struggle to generalize to dome-based warnings, leading to missed detections and false stops in safety-critical environments.
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