GuideTWSI: A Diverse Tactile Walking Surface Indicator Dataset from Synthetic and Real-World Images for Blind and Low-Vision Navigation

TL;DR

This paper introduces GuideTWSI, a comprehensive dataset of synthetic and real-world images of Tactile Walking Surface Indicators, aiming to improve segmentation and detection for blind and low-vision navigation across diverse environments.

Contribution

It provides the first large-scale, diverse dataset of TWSIs including both synthetic and real images, addressing the lack of varied viewpoints and geographic bias in existing datasets.

Findings

Enhanced TWSI detection accuracy with the new dataset

Improved generalization across different TWSI types and environments

Demonstrated the dataset's effectiveness in real-world navigation tasks

Abstract

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.