Computer Vision-Based Safety Monitoring of Mobile Scaffolding Integrating Depth Sensors

Mobile scaffolding is essential in construction but presents significant safety risks, particularly falls from height (FFH) due to improper use and insufficient monitoring. While prior research has identified hazards, it often lacks robust, actionable solutions, especially regarding the comprehensive analysis of worker behaviors and the spatial context. This study proposed a computer vision-based safety monitoring system that leverages depth cameras for accurate spatial assessments and incorporates temporal conditions to reduce false alarms. The proposed system extends object detection algorithms with mathematical logic derived from safety rules to classify four key unsafe conditions related to safety helmet use, guardrail and outrigger presence, and worker overcrowding on mobile scaffolds. A diverse dataset from multiple sources enhances the model’s applicability to real-world scenarios, while a status trigger module verifies worker behavior over a 3 s window, minimizing detection errors. The experimental results demonstrate high precision (0.95), recall (0.97), F1-score (0.96), and accuracy (0.95) for safe behaviors, with similarly strong metrics for unsafe behaviors. The qualitative analysis further confirms substantial improvements in worker position detection and safety compliance using 3D data over 2D approaches. These findings highlight the effectiveness of the proposed system in improving mobile scaffolding safety, addressing critical research gaps, and advancing construction industry safety standards.