ArticleDisability and rehabilitation. Assistive technology2026
Robust computer-vision based construction site detection for assistive-technology applications.
Article in Disability and rehabilitation. Assistive technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Residual gaze behaviour during navigation in blindness and low vision.Disability and rehabilitation. Assistive technology · 2026Review
- Evaluating OCR performance for assistive technology: effects of walking speed, camera placement, and camera type.Disability and rehabilitation. Assistive technology · 2026Article
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Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeNavigating urban environments poses significant challenges for individuals who are blind or have low vision, especially in areas affected by construction. Construction zones introduce hazards such as uneven surfaces, barriers, hazardous materials, excessive noise, and altered routes that obstruct familiar paths and compromise safety. Although navigation tools assist in trip planning, they often overlook these temporary obstacles. Existing hazard detection systems also struggle with the visual variability of construction sites.
methodsWe developed a computer vision-based assistive system integrating three modules: an open-vocabulary object detector to identify diverse construction-related elements, a YOLO-based model specialised in detecting scaffolding and poles, and an optical character recognition module to interpret construction signage.
resultsIn static testing at seven construction sites using images from multiple stationary viewpoints, the system achieved 88.56% overall accuracy. It consistently identified relevant objects within 2-10 m and at approach angles up to 75°. At 2-4 m, detection was perfect (100%) across all angles. Even at 10 m, six of seven sites remained detectable within a 15° approach. In dynamic testing along a 0.5-mile urban route containing eight construction sites, the system analysed every frame of a first-person walking video. It achieved 87.26% accuracy in distinguishing construction from non-construction areas, rising to 92.0% with a 50-frame majority vote filter.
conclusionThe system can reliably detect construction sites in real time and at sufficient distances to provide advance warnings, enabling individuals with visual impairments to make safer mobility decisions, such as proceeding with caution or rerouting.
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