# A statistical method for crack pre-detection in 3D concrete images

arXiv: https://arxiv.org/abs/2402.16126 · HTML: https://mlsanity.com/paper/2402.16126/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 25 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [An Investigation into Neural Net Optimization via Hessian Eigenvalue Density](https://mlsanity.com/paper/1901.10159/) (2019)
- [AdaPT: An interactive procedure for multiple testing with side information](https://mlsanity.com/paper/1609.06035/) (2016)
- [Multiple testing with the structure adaptive Benjamini-Hochberg algorithm](https://mlsanity.com/paper/1606.07926/) (2016)
- [Crack Modeling via Minimum-Weight Surfaces in 3d Voronoi Diagrams](https://mlsanity.com/paper/2210.05093/) (2022)
- [Methods for segmenting cracks in 3d images of concrete: A comparison based on semi-synthetic images](https://mlsanity.com/paper/2112.09493/) (2021)
- [Fast and Fair Simultaneous Confidence Bands for Functional Parameters](https://mlsanity.com/paper/1910.00131/) (2019)
- [Optimal False Discovery Rate Control for Large Scale Multiple Testing with Auxiliary Information](https://mlsanity.com/paper/2103.15311/) (2021)
- [Detecting anomalies in fibre systems using 3-dimensional image data](https://mlsanity.com/paper/1907.06988/) (2019)
- [Local False Discovery Rate Based Methods for Multiple Testing of One-Way Classified Hypotheses](https://mlsanity.com/paper/1712.05014/) (2017)
- [Detecting negative eigenvalues of exact and approximate Hessian matrices in optimization](https://mlsanity.com/paper/2206.05318/) (2022)

## Abstract (arXiv)

In practical applications, effectively segmenting cracks in large-scale computed tomography (CT) images holds significant importance for understanding the structural integrity of materials. Classical image-processing techniques and modern deep-learning models both face substantial computational challenges when applied directly to high resolution big data volumes. This paper introduces a statistical framework for crack pre-localization, whose purpose is not to replace or compete with segmentation networks, but to identify, with controlled error rates, the regions of a 3D CT image that are most likely to contain cracks. The method combines a simple Hessian-based filter, geometric descriptors computed on a regular spatial partition, and a spatial multiple testing procedure to detect anomalous regions while relying only on minimal calibration data, rather than large annotated datasets. Experiments on semi-synthetic and real 3D CT scans demonstrate that the proposed approach reliably highlights regions likely to contain cracks while preserving linear computational complexity. By restricting subsequent high resolution segmentation to these localized regions, deep-learning models can be trained and operate more efficiently, reducing both training runtime as well as resource consumption. The framework thus offers a practical and interpretable preprocessing step for large-scale CT inspection pipelines.