  Curbs serve as vital borders that delineate safe pedestrian zones from
potential vehicular traffic hazards. Curbs also represent a primary spatial
hazard during dynamic navigation with significant stumbling potential. Such
vulnerabilities are particularly exacerbated for persons with blindness and low
vision (PBLV). Accurate visual-based discrimination of curbs is paramount for
assistive technologies that aid PBLV with safe navigation in urban
environments. Herein, we investigate the efficacy of curb segmentation for
foundation models. We introduce the largest curb segmentation dataset to-date
to benchmark leading foundation models. Our results show that state-of-the-art
foundation models face significant challenges in curb segmentation. This is due
to their high false-positive rates (up to 95%) with poor performance
distinguishing curbs from curb-like objects or non-curb areas, such as
sidewalks. In addition, the best-performing model averaged a 3.70-second
inference time, underscoring problems in providing real-time assistance. In
response, we propose solutions including filtered bounding box selections to
achieve more accurate curb segmentation. Overall, despite the immediate
flexibility of foundation models, their application for practical assistive
technology applications still requires refinement. This research highlights the
critical need for specialized datasets and tailored model training to address
navigation challenges for PBLV and underscores implicit weaknesses in
foundation models.
