GuideTWSI: A Diverse Tactile Walking Surface Indicator Dataset from Synthetic and Real-World Images for Blind and Low-Vision Navigation
This work is significant for BLV pedestrians and guide dog handlers, as it aims to improve the reliability and accuracy of TWSI segmentation for navigation assistance, addressing a safety-critical need.
This paper addresses the lack of diverse datasets for Tactile Walking Surface Indicator (TWSI) segmentation, which are crucial for blind and low-vision (BLV) navigation. Existing datasets are geographically biased towards East Asian directional bars and lack robot-relevant viewpoints, leading to poor generalization for dome-based warnings common in North America and Europe.
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.