https://scholars.lib.ntu.edu.tw/handle/123456789/640040
Title: | Autonomous dimensional inspection and issue tracking of rebar using semantically enriched 3D models | Authors: | Chang, Chun Cheng Huang, Tsung Wei Chen, Yi Hsiang JACOB JE-CHIAN LIN CHUIN-SHAN CHEN |
Keywords: | Autonomous | Building information modeling | Computer vision | Deep learning | Digital twin | Dimensional quality control | Rebar inspection | Issue Date: | 1-Apr-2024 | Journal Volume: | 160 | Source: | Automation in Construction | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/640040 | ISSN: | 09265805 | DOI: | 10.1016/j.autcon.2024.105303 |
Appears in Collections: | 土木工程學系 |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.