Vision-Based Productivity Monitoring of Tower Crane Operations during Curtain Wall Installation Using a Database-Free Approach

Abstract

Tower cranes are generally used in modern construction projects due to their exclusive and flexible performance for material transportation in complex workspaces. Although traditional deep learning vision-based methods have proven their effectiveness in construction site monitoring, a shortage of realistic training images and inefficiencies in them have been an obstacle in achieving effective tower crane monitoring. To deal with this issue, this paper presents a database-free approach for timely productivity monitoring of tower crane operations. Using a synthetic model, the authors construct a research framework to effectively generate training images on deep learning–powered object detection. Additionally, an enhanced tracking algorithm collaborating with unsupervised clustering-driven postprocessing enables stable performance for tracking resources. Further, logical ontology-based activity recognition constructed on domain-specific knowledge takes place to estimate time-to-time operation of the tower crane. The curtainwall installation was selected to represent the dynamicity and variability of tower crane operations for which it has been difficult to apply traditional deep learning technologies. Results displayed that the intended framework successfully produced 300,000 training images in 1 h, about 500 times faster than human resources, and an enhanced tracker performed well even in the case of unsatisfactory detection results. It is also proved that a performance of activity analysis was acceptable to 93.58% accuracy. The results demonstrate that the proposed framework has proven remarkable data generation speed with little manual input and good performance of operational-level activity analysis. These findings can automate the monitoring process of tower cranes’ operations and the productivity of curtainwall installation, assisting construction project decision making. Furthermore, such efficient methods are less time-consuming and can handle background scene changes effortlessly, building customized models in less time and to less cost, thus buffering the practical gap of applying vision-based technologies into visually dynamic construction sites.