mlsanityData to 14 Oct 2026

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

arXiv 2401.03298 ↗First posted 6 Jan 2024cs.CVcited by 1

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Abstract (arXiv)

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).

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 67 references that are in the map. First posted on arXiv 6 Jan 2024.

Builds on

24 of 67 references.

References · 24

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Surveycited by 3