Back to Search
Start Over
Integrated vision-based automated progress monitoring of indoor construction using mask region-based convolutional neural networks and BIM.
- Source :
-
Automation in Construction . Aug2022, Vol. 140, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
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. • Described an evaluation framework to recognize wall progress of an entire floor. • Proposed an image segmentation method to calculate wall construction area. • Improved Mask R-CNN algorithm to increase segmentation accuracy. • Discussed the influence of image light on segmentation accuracy. • Applied the framework to a case study with mean Average Precision of 96.8%. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09265805
- Volume :
- 140
- Database :
- Academic Search Index
- Journal :
- Automation in Construction
- Publication Type :
- Academic Journal
- Accession number :
- 157352742
- Full Text :
- https://doi.org/10.1016/j.autcon.2022.104327