Utilization of Computer Vision Applications in Construction Safety

Utilization of Computer Vision Applications in Construction Safety White Paper The construction industry remains one of the most hazardous sectors worldwide, accounting for a significant proportion of workplace injuries and fatalities. Traditional safety management practices primarily rely on manual inspections, safety audits, and human supervision, which can be time-consuming, subjective, and insufficient for monitoring large and dynamic construction environments. Recent advancements in Artificial Intelligence (AI) and Computer Vision (CV) have enabled automated, real-time monitoring systems capable of improving safety performance and reducing accident risks. This white paper explores the utilization of computer vision applications in construction safety, highlighting how image processing, deep learning, object detection, and activity recognition technologies are transforming safety management practices. Computer vision systems can automatically identify workers, detect personal protective equipment (PPE) compliance, monitor unsafe behaviors, track worker locations, identify hazardous conditions, and generate real-time alerts from video streams captured through CCTV cameras, drones, and mobile devices. Key applications include helmet detection, safety jacket detection, safety shoe compliance monitoring, fall detection, restricted-area intrusion detection, worker-machine interaction monitoring, and construction site hazard identification. Advanced deep learning models such as YOLO, Faster R-CNN, Mask R-CNN, and Vision Transformers have demonstrated high accuracy in detecting safety violations under diverse site conditions.