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

Christian Benz, Volker Rodehorst
1/6/2024
cs.CV

No official code found. Best implementation: ben-z-original/enstrect (9 stars, last commit 2025-07-16).

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).

View on arXivView PDF

Code Implementations(1)

ben-z-original/enstrectCommunity100%

A three-stage pipeline for image-based structural inspection

90PythonAug 4, 20241 years ago

Similar Papers

SDIGLM: Leveraging Large Language Models and Multi-Modal Chain of Thought for Structural Damage Identification

84% match2025

Unified Few-shot Crack Segmentation and its Precise 3D Automatic Measurement in Concrete Structures

83% match2025

Addressing the Pitfalls of Image-Based Structural Health Monitoring: A Focus on False Positives, False Negatives, and Base Rate Bias

81% match2024

Generalizable Disaster Damage Assessment via Change Detection with Vision Foundation Model

80% match2024

A Storage-Efficient Feature for 3D Concrete Defect Segmentation to Replace Normal Vector

80% match2025

Deep learning-based automated damage detection in concrete structures using images from earthquake events

80% match2025

Cite this paper

@article{benz2024enstrect,
  title  = {ENSTRECT: A Stage-based Approach to 2.5D Structural Damage Detection},
  author = {Christian Benz and Volker Rodehorst},
  year   = {2024},
  eprint = {2401.03298},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.03298v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2401.03298.svg)](https://codeofpaper.com/paper/2401.03298)

Discussion