AbstractManual building damage inspection in a postearthquake environment is typically resource-consuming and prone to limitations based on subjectivity. A bilevel thresholding–based iterative framework is proposed to automate the delineation of concrete-spalling using three-dimensional (3D) point-cloud data. Point-level surface variation was used for damage point characterization, and two stopping conditions were defined for process automation. Synthetic building element data with varying point distribution and damage region characteristics were used for quantitative analysis and comparison with the state-of-the-art iterative refinement analysis. Comparative analysis demonstrated that the proposed algorithm rendered damaged region detection with improved completeness and correctness. In this study, use of Matthews correlation coefficient (MCC) and mean generalized intersection over union (mGIoU) metrics for performance evaluation is proposed. For low-noise rectangular-wall samples used, an average increase of 20% and 55% MCC value compared with generalized iterative refinement analysis was observed for Stopping conditions I and II, respectively. Similarly, an average increase of 31% and 21% mGIoU was observed. Furthermore, the proposed algorithm has the potential to generalize better compared with the state-of-the-art because it does not require tuning or training using ground-truth data.

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