Automated Defect Detection on Dry-Hanging Stone Curtain Walls through Colored Point Clouds

• Published in: Buildings (Basel) Vol. 14; no. 9; p. 2652
• Main Authors: Yao, Zhidong, Li, Xuelai, Yan, Guihai, Lin, Zhongliang, Wang, Gang, Liu, Changyong, Yang, Xincong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2024
• Subjects: Accident prevention; Algorithms; Anomalies; Architecture; Artificial intelligence; Automation; Cement; colored point cloud; Computer vision; crack detection; Curtain walls; Defects; Enclosures; Endoscopy; Flaw detection; High rise buildings; Information processing; Inspection; Inspections; irregularity detection; Lasers; Methods; Scanning devices; Sealing compounds; Stone; stone curtain wall; Technology application; Three dimensional models; Unevenness; unevenness detection; China
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings14092652

Stone curtain walls are widely used in contemporary architectures; however, their regular inspection is always labor-intensive, time-consuming, and hazardous due to the complex and enclosed spatial structure of these high-rise building enclosures. To address this issue, this study proposes an automated and novel inspection method, which is composed of the following three steps: First, we utilize 3D laser scanning technology to capture colored point cloud data of the stone curtain wall system; subsequently, by extracting and processing the integration of color and depth information, the stone panels and end sealants are precisely segmented; finally, various defects, such as cracks, unevenness, and irregularities, are automatically identified through artificial intelligence algorithms in a timely manner. To validate the proposed method, an on-site experiment was carried out to demonstrate the effectiveness in detecting multiple defects concurrently on stone curtain walls. The experimental results showed that our proposed method could provide a non-contact and automated inspection alternative for all the stone curtain walls with a high accuracy of anomaly detection, facilitating rational maintenance plans and strategies to ensure the safety and performance of these modern building enclosures.