Vision-Based Framework for Automatic Progress Monitoring of Precast Walls by Using Surveillance Videos during the Construction Phase

Detecting and locating precast components to confirm and update components' status information is the key to construction progress monitoring. There are several ways to collect the information of elements in construction sites, such as manual collection, laser scanning, and tag-based methods. But each of these methods has its own limitations. Considering that effective and accurate construction progress monitoring is fundamental to construction management, this paper proposes a novel framework that integrates the latest computer vision methods to realize automatically monitoring construction progress of precast walls, one of the essential components in precast construction. In this framework, object detection, instance segmentation, and multiple-object tracking are combined to collect precast walls' location and temporal information from the surveillance videos recording the construction phase. Status information identified and collected is stored as a JavaScript object notation (JSON) format and then sent into a corresponding building information model (BIM) to timestamp the wall components. Each method in the framework is evaluated, respectively, and the demonstration on a real project proves the feasibility, convenience, and efficiency of this vision-based framework. The research results prove the proposed framework's ability to monitor the construction progress of precast walls automatically. Furthermore, the vital information extracted by the proposed framework contributes to serving application scenarios of the cyber-physical system in construction sites.