The installation of barricades effectively prevents falls from height (FFH) on construction sites. Common approaches for detecting missing barricades (e.g., manual inspection of the site or three-dimensional models) are not practical due to two inherent challenges: (1) these approaches are labor-intensive and time-consuming; and (2) FFH hazards are dynamic and changing as construction work progresses. To address these challenges, two computer vision-based detection approaches, including Masks Comparison Approach (MCA) and Missing Object Detection Approach (MODA), are developed in this study to automatically detect missing barricade. The performance of the proposed approaches and their benefits and implementation challenges were evaluated through a case study. The results demonstrate that MODA can achieve better performance and have several implementation advantages over MCA. The average precision and average recall for MODA were 57.9% and 73.6%, respectively. These two approaches can help site managers take action promptly to reduce the risks of FFH accidents.

路障的安装有效地防止了建筑工地的高处坠落 (FFH)。由于两个固有的挑战，用于检测丢失路障的常用方法（例如，手动检查现场或三维模型）并不实用：（1）这些方法是劳动密集型且耗时的；(2) FFH 危害是动态变化的，随着施工工作的进展而变化。为了应对这些挑战，两种基于计算机视觉的检测方法，包括掩码比较方法(MCA) 和丢失对象检测方法(MODA)，在这项研究中开发，以自动检测丢失的路障。通过案例研究评估了所提议方法的性能及其好处和实施挑战。结果表明，MODA 可以实现更好的性能，并且比 MCA 有几个实现优势。MODA 的平均准确率和平均召回率分别为 57.9% 和 73.6%。这两种方法可以帮助现场管理人员迅速采取行动，降低 FFH 事故的风险。