Autonomous dimensional inspection and issue tracking of rebar using semantically enriched 3D models
Journal
Automation in Construction
Journal Volume
160
Journal Volume
160
Start Page
105303
ISSN
09265805
Date Issued
2024-04
Author(s)
Abstract
Accurate and efficient inspection of rebar dimensions has proven to be a persistent challenge for researchers and practitioners. This paper introduces a semantically enriched 3D model-based system that employs computer vision and deep learning for location-aware identification and tracking of rebar issues. The system comprises four modules: (A) digital twin generation, (B) segmentation, (C) inspection, and (D) issue identification and tracking. The generation module constructs 3D models from rebar structures. The segmentation and inspection modules analyze the 3D models, enriching them with semantic information. The issue identification and tracking module exchanges information between the semantically enriched 3D models and the building information models across time. An experiment on a column rebar cage is conducted. A precision of over 90% and a recall of over 97% are reported in 3D instance segmentation. Diameter inspection achieves an accuracy of 95.5% for large-size rebars. Spacing inspection achieves a mean relative error of 0.98%. The defective spacing is identified and tracked.
Subjects
Autonomous
Building information modeling
Computer vision
Deep learning
Digital twin
Dimensional quality control
Rebar inspection
Publisher
Elsevier B.V.
Type
journal article