mlsanityData to 7 Oct 2026

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

arXiv 2202.03714 ↗First posted 8 Feb 2022cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 35 of its 135 references that are in the map. First posted on arXiv 8 Feb 2022.

Builds on

35 of 135 references.

References · 35

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23 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2023Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulationcited by 10
  2. 2025CrossDiff: Diffusion Probabilistic Model With Cross-conditional Encoder-Decoder for Crack Segmentationcited by 4
  3. 2024Multi-temporal crack segmentation in concrete structures using deep learning approachescited by 0
  4. 2026Rapid-Deployment Crack Measurement Based on SAM3 Semantic-Edge Response Decodingcited by 0