[论文解读] Using Unmanned Aerial Systems (UAS) for Assessing and Monitoring Fall Hazard Prevention Systems in High-rise Building Projects

Yimeng Li, Behzad Esmaeili+3 more|arXiv (Cornell University)|Sep 27, 2022

Infrastructure Maintenance and Monitoring被引用 4

一句话总结

本文提出了一种基于机器学习的UAS框架，用于检测和监控高层建筑施工现场临边区域附近的护栏立柱。通过采用目标检测、楼层定位和立柱间距分析的级联结构，该系统提高了识别防坠落危险防护系统时的精确率和召回率，使工程师能够实现自动化安全评估。

ABSTRACT

This study develops a framework for unmanned aerial systems (UASs) to monitor fall hazard prevention systems near unprotected edges and openings in high-rise building projects. A three-step machine-learning-based framework was developed and tested to detect guardrail posts from the images captured by UAS. First, a guardrail detector was trained to localize the candidate locations of posts supporting the guardrail. Since images were used in this process collected from an actual job site, several false detections were identified. Therefore, additional constraints were introduced in the following steps to filter out false detections. Second, the research team applied a horizontal line detector to the image to properly detect floors and remove the detections that were not close to the floors. Finally, since the guardrail posts are installed with approximately normal distribution between each post, the space between them was estimated and used to find the most likely distance between the two posts. The research team used various combinations of the developed approaches to monitor guardrail systems in the captured images from a high-rise building project. Comparing the precision and recall metrics indicated that the cascade classifier achieves better performance with floor detection and guardrail spacing estimation. The research outcomes illustrate that the proposed guardrail recognition system can improve the assessment of guardrails and facilitate the safety engineer's task of identifying fall hazards in high-rise building projects.

研究动机与目标

• 开发一种基于UAS的自动化系统，用于评估高层建筑项目中的坠落危险防护系统。
• 解决从航拍图像中检测护栏立柱的挑战，包括误报和立柱间距不一致的问题。
• 通过减少对人工检查的依赖，提高安全监控效率。
• 整合空间约束（楼层对齐和立柱间距）以提升检测精度。
• 使用来自高层建筑施工现场的真实航拍图像验证该框架的性能。

提出的方法

• 训练了一个基于YOLO的护栏检测器，以在UAS拍摄的图像中定位候选护栏立柱。
• 应用水平线检测器识别楼层位置，并过滤掉未与楼层对齐的检测结果。
• 通过利用立柱的期望正态分布进行立柱间距估计，以验证和优化检测结果。
• 采用级联分类器结合检测结果、楼层对齐和间距约束，以减少误报。
• 使用精确率和召回率指标，在真实高层建筑施工现场的航拍图像上测试了该框架。
• 评估了检测、楼层检测和间距估计的各种组合，以优化性能。

实验结果

研究问题

• RQ1 UAS影像结合机器学习能否有效检测高层建筑施工中临边区域附近的护栏立柱？
• RQ2 楼层对齐和立柱间距等空间约束如何提升检测精度？
• RQ3 结合楼层检测和间距估计的级联检测系统在真实环境中的表现如何？
• RQ4 与独立检测相比，所提出的框架在多大程度上减少了误报？
• RQ5 多种约束的整合如何影响护栏监测中的精确率和召回率？

主要发现

• 集成楼层检测和立柱间距估计的级联分类器在所有测试配置中实现了最高的精确率和召回率。
• 楼层对齐的整合通过过滤掉未位于结构楼层附近的立柱，显著减少了误检。
• 立柱间距估计有助于识别最可能有效的立柱配对，从而提高了检测结果的置信度。
• 该系统在复杂视觉杂波和光照变化多变的真实环境中表现出更强的鲁棒性。
• 该框架通过自动化护栏系统的检测，显著减少了人工检查的工作量。
• 结果表明，结合多种约束可显著提升安全监测应用的可靠性。