Towards automatic crack segmentation in 3d concrete images

Jung, Christian; Müsebeck, Franziska; Barisin, Tin; Schladitz, Katja; Redenbach, Claudia; Kiesche, Martin; Pahn, Matthias

doi:10.58286/26620

Cited by 5 publications

(14 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The crack segmentation methods of choice were 3d U-net, Hessianbased percolation, and template matching. Compared to previous studies [3,4,14], their parameters were only varied marginally to validate the methods' robustness. However, some adaptions are necessary to account for the cracks' scales.…”

Section: Resultsmentioning

confidence: 99%

“…The performance of several crack segmentation methods has been analyzed and compared on simulated cracks in 3d CT images of concrete [3]. Based on these results, the methods have successfully been adapted to authentic cracks [4,14]. Here, our goal is a qualitative comparison of the methods with those previously developed for industrial purposes [25].…”

Section: Crack Segmentation Methodsmentioning

confidence: 99%

“…The best-performing methods were Hessian-based percolation [9] and the 3d U-net [6]. Subsequently, both approaches were successfully adapted to real CT data of concrete [4,14]. Cracks in concrete can occur due to various causes such as mechanical load [11,12], alkali-silica reaction (ASR) [28,29], freezeand-thaw cycles [27], corrosion [21,26] or fire exposition [20].…”

Section: Introductionmentioning

confidence: 99%

“…350 voxels. The parameter σ is tuned accordingly and with the same reasoning as in [14]. We set σ min = 0.5, σ max = 175 and choose a σ -step size of 0.…”

mentioning

confidence: 99%

“…A classical choice is η σ = 2(max p R(p, σ )) 2 [10]. A previous study has shown that this choice also suppresses thin crack structures if the concrete matrix contains bright artifacts [14]. Therefore, we use η σ = 2(R 0.99 (p, σ )) 2 for both images where R 0.99 (p, σ ) is the 99%-quantile of the empirical distribution of R given in Equation (1).…”

mentioning

confidence: 99%

See 4 more Smart Citations

3d imaging and analysis of cracks in loaded concrete samples

Jung¹,

Nowacka²,

Barisin³

et al. 2023

eJNDT

View full text Add to dashboard Cite

Concrete plays a central role as the standard building material in civil engineering. Experimental characterization of the concrete microstructure and a description of failure mechanisms are important to understand the concrete’s mechanical properties. Computed tomography is a powerful source of information as it yields 3d images of concrete specimens. However, complete visual inspection is often infeasible due to very large image sizes. Hence, automatic methods for crack detection and segmentation are needed. A region-growing algorithm and a 3d U-Net showed promising results in a previous study. Cracks in normal concrete and high-performance concrete that were initiated via tensile tests were investigated. Here, the methods are validated on a more diverse set of concrete types and crack characteristics. Adequate adaptions of the methods are necessary to deal with the complex crack structures. The segmentation results are assessed qualitatively and compared to those of a template matching algorithm which is well-established in industry.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Crack Segmentation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…350 voxels. The parameter σ is tuned accordingly and with the same reasoning as in [14]. We set σ min = 0.5, σ max = 175 and choose a σ -step size of 0.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

3d imaging and analysis of cracks in loaded concrete samples

Jung¹,

Nowacka²,

Barisin³

et al. 2023

eJNDT

View full text Add to dashboard Cite

show abstract

Cracks in Concrete

Barisin,

Jung,

Nowacka

et al. 2024

Lecture Notes in Statistics

View full text Add to dashboard Cite

Riesz Networks: Scale-Invariant Neural Networks in a Single Forward Pass

Barisin,

Schladitz,

Redenbach

2024

J Math Imaging Vis

View full text Add to dashboard Cite

Scale invariance of an algorithm refers to its ability to treat objects equally independently of their size. For neural networks, scale invariance is typically achieved by data augmentation. However, when presented with a scale far outside the range covered by the training set, neural networks may fail to generalize. Here, we introduce the Riesz network, a novel scale- invariant neural network. Instead of standard 2d or 3d convolutions for combining spatial information, the Riesz network is based on the Riesz transform which is a scale-equivariant operation. As a consequence, this network naturally generalizes to unseen or even arbitrary scales in a single forward pass. As an application example, we consider detecting and segmenting cracks in tomographic images of concrete. In this context, ‘scale’ refers to the crack thickness which may vary strongly even within the same sample. To prove its scale invariance, the Riesz network is trained on one fixed crack width. We then validate its performance in segmenting simulated and real tomographic images featuring a wide range of crack widths. An additional experiment is carried out on the MNIST Large Scale data set.

show abstract

Towards automatic crack segmentation in 3d concrete images

Cited by 5 publications

References 16 publications

3d imaging and analysis of cracks in loaded concrete samples

3d imaging and analysis of cracks in loaded concrete samples

Cracks in Concrete

Riesz Networks: Scale-Invariant Neural Networks in a Single Forward Pass

Contact Info

Product

Resources

About