用视觉技术实时识别施工区域，帮视障者提前避险。
Robust Computer-Vision based Construction Site Detection for Assistive-Technology Applications
- 融合三模块：开放词汇检测、专用支架检测、文字识别。
- 静态测试准确率88.56%，2-4米内检测率达100%。
- 适合开发无障碍导航工具，尤其关注城市施工区安全。
目的：城市环境中，盲人或低视力人群在施工区域面临显著挑战，如不平路面、障碍物、危险材料、噪音及路径改变。现有导航工具常忽略临时障碍，而现有危险检测系统难以应对施工场景的视觉多样性。方法：我们开发了一套基于计算机视觉的辅助系统，包含三个模块：开放词汇目标检测器用于识别多样施工元素，基于YOLO的模型专注检测脚手架与立杆，光学字符识别模块用于解析施工标识。结果：在七个施工场地的静态测试中，系统整体准确率达88.56%。在2–10米距离内，可稳定识别对象，最大识别角度达75°；2–4米时，所有角度下检测准确率均为100%。即使在10米处，六处场地仍能在15°视角内被识别。动态测试沿0.5英里路线进行，包含八处施工点，系统分析第一人称行走视频每帧画面，未滤波准确率为87.26%，经50帧多数表决过滤后提升至92.0%。结论：该系统能实时、可靠地检测施工区域，且在足够远距离提供预警，使视障者可提前采取谨慎行动或重新规划路线，提升出行安全性。
原文摘要 · Abstract (English)
Purpose: Navigating 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. Methods: We 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 specialized in detecting scaffolding and poles, and an optical character recognition module to interpret construction signage. Results: In 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 meters and at approach angles up to 75$^{\circ}$. At 2--4 meters, detection was perfect (100%) across all angles. Even at 10 meters, six of seven sites remained detectable within a 15$^{\circ}$ approach. In dynamic testing along a 0.5-mile urban route containing eight construction sites, the system analyzed 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. Conclusion: The 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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