Robust Computer-Vision based Construction Site Detection for Assistive-Technology Applications

Navigating urban environments poses significant challenges for people with disabilities, particularly those with blindness and low vision. Environments with dynamic and unpredictable elements like construction sites are especially challenging. Construction sites introduce hazards like uneven surfaces, obstructive barriers, hazardous materials, and excessive noise, and they can alter routing, complicating safe mobility. Existing assistive technologies are limited, as navigation apps do not account for construction sites during trip planning, and detection tools that attempt hazard recognition struggle to address the extreme variability of construction paraphernalia. This study introduces a novel computer vision-based system that integrates open-vocabulary object detection, a YOLO-based scaffolding-pole detection model, and an optical character recognition (OCR) module to comprehensively identify and interpret construction site elements for assistive navigation. In static testing across seven construction sites, the system achieved an overall accuracy of 88.56\%, reliably detecting objects from 2m to 10m within a 0$^\circ$ -- 75$^\circ$ angular offset. At closer distances (2--4m), the detection rate was 100\% at all tested angles. At

Can Foundation Models Reliably Identify Spatial Hazards? A Case Study on Curb Segmentation

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.