Automatic detection of falling hazard from surveillance videos based on computer vision and building information modeling

Abstract

In construction sites, safety issue has always been a threatening risk that must be taken seriously. In order to improve the safety management capability of construction site, this paper establishes a construction site fall hazard management system. This system takes computer vision instead of sensor systems as an information exchange bridge between the physical construction site and the virtual model. The main contribution of this paper is to propose a deep learning-based approach to achieve bi-directional information exchange from physical construction sites to digital models. To train the deep learning model, this paper builds a dataset containing approximately 4000 images from multiple sources to detect on-site safety risks. This paper also proposes a method for acquiring coordinates of workers and risk sources, then establishes an automated safety management framework based on the real-time locations of risk sources and workers. The effectiveness of this safety management framework is verified through a case study comparing the differences between safety officers and the automated system. In addition, the method proposed in this paper integrating building information modeling (BIM) with the real construction process, achieving bi-directional coordination between the digital level and the physical level, providing data basis for intelligent construction.