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This study develops a framework for unmanned aerial systems (UASs) to monitorfall hazard prevention systems near unprotected edges and openings in high-risebuilding projects. A three-step machine-learning-based framework was developedand tested to detect guardrail posts from the images captured by UAS. First, aguardrail detector was trained to localize the candidate locations of postssupporting the guardrail. Since images were used in this process collected froman actual job site, several false detections were identified. Therefore,additional constraints were introduced in the following steps to filter outfalse detections. Second, the research team applied a horizontal line detectorto the image to properly detect floors and remove the detections that were notclose to the floors. Finally, since the guardrail posts are installed withapproximately normal distribution between each post, the space between them wasestimated and used to find the most likely distance between the two posts. Theresearch team used various combinations of the developed approaches to monitorguardrail systems in the captured images from a high-rise building project.Comparing the precision and recall metrics indicated that the cascadeclassifier achieves better performance with floor detection and guardrailspacing estimation. The research outcomes illustrate that the proposedguardrail recognition system can improve the assessment of guardrails andfacilitate the safety engineer's task of identifying fall hazards in high-risebuilding projects.

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