ENSTRECT: A Stage-based Approach to 2.5D Structural Damage Detection

2024-01-06 · Christian Benz, Volker Rodehorst

To effectively assess structural damage, it is essential to localize the instances of damage in the physical world of a civil structure. ENSTRECT is a stage-based approach designed to accomplish 2.5D structural damage detection. The method requires an image collection, the relative orientation, and a point cloud. Using these inputs, surface damages are segmented at the image level and then mapped into the point cloud space, resulting in a segmented point cloud. To enable further quantitative analyses, the segmented point cloud is transformed into measurable damage instances: cracks are extracted by contracting the clustered point cloud into a corresponding medial axis. For areal damages, such as spalling and corrosion, a procedure is proposed to compute the bounding polygon based on PCA and alpha shapes. With a localization tolerance of 4cm, ENSTRECT can achieve IoUs of over 90% for cracks, 82% for corrosion, and 41% for spalling. Detection at the instance level yields an AP50 of about 45% (cracks, spalling) and 56% (corrosion).

📄 PDF Abstract BibTeX arXiv:2401.03298

Code (1)

ben-z-original/enstrect 공식 구현 pytorch

Tasks

3D Instance Segmentation Instance Segmentation Semantic Segmentation

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