Preventing falls from floor openings using quadrilateral detection and construction worker pose-estimation

Abstract

This paper addressed safety risks in the construction industry, emphasizing the prevalent and fatal risk of falls from heights due to floor openings. Although advancements in computer vision and deep learning offer opportunities for automated safety monitoring, challenges such as inaccuracies in object localization and measuring distances to unsafe zones persist. To overcome these issues, a detection method employing convex quadrilateral bounding boxes was presented, taking into account perspective changes from the view of the camera. By leveraging a pretrained pose-estimation model and enhancing the YOLOv7 architecture, the new method precisely identified unsafe areas and generated virtual fences around floor openings. The presented approach resulted in an average precision of 80.55% and an F1-score of 86.49% in alerting dangers, outperforming existing techniques. This paper underscores the effective integration of state-of-the-art computer vision methodologies for practical safety monitoring in construction sites, highlighting its promise in accident prevention.