Cracks in concrete

TL;DR

This paper presents a semi-synthetic data generation approach and a scale-invariant CNN, RieszNet, for improved 3D crack segmentation in concrete images, addressing data scarcity and variability challenges.

Contribution

It introduces a semi-synthetic data generation method and the RieszNet architecture for scale-invariant crack segmentation in 3D concrete images.

Findings

Effective semi-synthetic data generation for training CNNs.

RieszNet achieves scale invariance in crack segmentation.

Method generalizes to different concrete types.

Abstract

Finding and properly segmenting cracks in images of concrete is a challenging task. Cracks are thin and rough and being air filled do yield a very weak contrast in 3D images obtained by computed tomography. Enhancing and segmenting dark lower-dimensional structures is already demanding. The heterogeneous concrete matrix and the size of the images further increase the complexity. ML methods have proven to solve difficult segmentation problems when trained on enough and well annotated data. However, so far, there is not much 3D image data of cracks available at all, let alone annotated. Interactive annotation is error-prone as humans can easily tell cats from dogs or roads without from roads with cars but have a hard time deciding whether a thin and dark structure seen in a 2D slice continues in the next one. Training networks by synthetic, simulated images is an elegant way out, bears however its own challenges. In this contribution, we describe how to generate semi-synthetic image data to train CNN like the well known 3D U-Net or random forests for segmenting cracks in 3D images of concrete. The thickness of real cracks varies widely, both, within one crack as well as from crack to crack in the same sample. The segmentation method should therefore be invariant with respect to scale changes. We introduce the so-called RieszNet, designed for exactly this purpose. Finally, we discuss how to generalize the ML crack segmentation methods to other concrete types.