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

Abstract

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.