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What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification

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arxiv 2202.03714 v1 pith:ZF76WZGO submitted 2022-02-08 cs.CV

Jacob König , Mark Jenkins , Mike Mannion , Peter Barrie , Gordon Morison This is my paper · ORCID
classification cs.CV
keywords algorithmscracksdetectionmonitoringstructuralcrackdeeplearning
verification ladder T0 review T1 audit T2 compute T3 formal
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Surface cracks are a very common indicator of potential structural faults. Their early detection and monitoring is an important factor in structural health monitoring. Left untreated, they can grow in size over time and require expensive repairs or maintenance. With recent advances in computer vision and deep learning algorithms, the automatic detection and segmentation of cracks for this monitoring process have become a major topic of interest. This review aims to give researchers an overview of the published work within the field of crack analysis algorithms that make use of deep learning. It outlines the various tasks that are solved through applying computer vision algorithms to surface cracks in a structural health monitoring setting and also provides in-depth reviews of recent fully, semi and unsupervised approaches that perform crack classification, detection, segmentation and quantification. Additionally, this review also highlights popular datasets used for cracks and the metrics that are used to evaluate the performance of those algorithms. Finally, potential research gaps are outlined and further research directions are provided.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Training-Free Semantic-Edge Response Decoding of SAM3 for Cross-Domain Infrastructure Crack Segmentation

    cs.CV 2026-07 unverdicted novelty 6.0 of 10

    SERD thresholds SAM3's internal semantic response combined with a fixed Sobel edge prior, improving cross-domain crack segmentation over native SAM3 by 3.3–3.7 IoU points without training.

  2. CrossDiff: Diffusion Probabilistic Model With Cross-conditional Encoder-Decoder for Crack Segmentation

    cs.CV 2025-01 conditional novelty 5.0 of 10

    CrossDiff applies a diffusion probabilistic model with a cross-conditional encoder-decoder to crack segmentation, reporting state-of-the-art Dice and IoU scores on five datasets.

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