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Computer Science > Computer Vision and Pattern Recognition

arXiv:2401.03298 (cs)
[Submitted on 6 Jan 2024 (v1), last revised 2 Oct 2024 (this version, v2)]

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

Authors:Christian Benz, Volker Rodehorst
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Abstract: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).
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2401.03298 [cs.CV]
  (or arXiv:2401.03298v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2401.03298

Submission history

From: Christian Benz [view email]
[v1] Sat, 6 Jan 2024 20:39:20 UTC (4,055 KB)
[v2] Wed, 2 Oct 2024 20:24:14 UTC (3,958 KB)
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