Integrated vision-based automated progress monitoring of indoor construction using mask region-based convolutional neural networks and BIM

Abstract

Traditional construction progress tracking relies on labor-intensive activities with time lags, potential man-made errors, and inefficient progress management, which demands for an innovative and automated progress tracking approach. This paper describes a deep learning method that utilizes image segmentation to automatically evaluate the wall construction progress of an entire floor with the progress results streamlined to BIM. The approach was applied to a case study in China for assessing plastering construction activities with high segmentation accuracy (mean average precision = 96.8%). Further improvement of Mask Region-Based Convolutional Neural Networks (Mask R-CNN) and evaluation of its superiority over other models have also been discussed. This study provides both theoretical and practical references for unmanned supervision of progress tracking and intelligent schedule management.