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

TL;DR

This paper presents a statistical pre-localization method for identifying potential crack regions in 3D CT images, enabling more efficient and targeted crack segmentation in large-scale structural analysis.

Contribution

It introduces a minimal-data, computationally efficient framework combining Hessian filtering, geometric descriptors, and multiple testing for crack region pre-detection in 3D CT scans.

Findings

Reliable identification of crack-containing regions in 3D CT images.

Linear computational complexity ensures scalability.

Reduces training time and resource use for deep learning segmentation.

Abstract

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.