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Crack Identification Based on Computer Vision
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Crack Identification Based on Computer Vision

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (1 June 2025) | Viewed by 1164

Special Issue Editor

Dr. Chuanqi Li

E-Mail Website
Guest Editor
School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Interests: high-performance concrete; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the construction and operation of traffic and geotechnical structures, the generation of cracks on the surfaces of structures and rock masses poses a significant threat to the safety of engineering. The accurate identification of cracks is essential in evaluation safety in a timely manner and enhancing the stability of engineering. Driven by the vigorous development of computer performance, computer vision technology takes the initiative in the realization of the above goals. Therefore, the aim of this Special Issue is to present the application of novel technology and concepts based on computer vision in crack identification.

The scope of this Special Issue includes, but is not limited to, the following topics: crack detection in civil structures, crack identification in surface or underground rock mass, crack detection in concrete structures, the identification of the location of material fatigue crack initiation, railway track visual inspection and crack detection, pavement crack detection and identification, bridge crack automatic detection, and crack identification in other transportation infrastructure surfaces. In addition, we welcome the submission of articles that present research on the combination of computer vision techniques and artificial intelligence models.

Dr. Chuanqi Li
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computer vision
  • crack identification
  • artificial intelligence models

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Published Papers (2 papers)

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Research

16 pages, 21150 KiB  
Article
STCYOLO: Subway Tunnel Crack Detection Model with Complex Scenarios
by Jia Zhang, Hui Li, Weidong Song, Jinhe Zhang and Miao Shi
Information 2025, 16(6), 507; https://doi.org/10.3390/info16060507 - 18 Jun 2025
Viewed by 293
Abstract
The detection of tunnel cracks plays a vital role in ensuring structural integrity and driving safety. However, tunnel environments present significant challenges for crack detection, such as uneven lighting and shadow occlusion, which can obscure surface features and reduce detection accuracy. To address [...] Read more.
The detection of tunnel cracks plays a vital role in ensuring structural integrity and driving safety. However, tunnel environments present significant challenges for crack detection, such as uneven lighting and shadow occlusion, which can obscure surface features and reduce detection accuracy. To address these challenges, this paper proposes a novel crack detection network named STCYOLO. First, a dynamic snake convolution (DSConv) mechanism is introduced to adaptively adjust the shape and size of convolutional kernels, allowing them to better align with the elongated and irregular geometry of cracks, thereby enhancing performance under challenging lighting conditions. To mitigate the impact of shadow occlusion, a Shadow Occlusion-Aware Attention (SOAA) module is designed to enhance the network’s ability to identify cracks hidden in shadowed regions. Additionally, a tiny crack upsampling (TCU) module is proposed, which reorganizes convolution kernels to more effectively preserve fine-grained spatial details during upsampling, thereby improving the detection of small and subtle cracks. The experimental results demonstrate that, compared to YOLOv8, our proposed method achieves a 2.85% improvement in mAP and a 3.02% increase in the F score on the crack detection dataset. Full article
(This article belongs to the Special Issue Crack Identification Based on Computer Vision)
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24 pages, 103560 KiB  
Article
Automated Crack Width Measurement in 3D Models: A Photogrammetric Approach with Image Selection
by Huseyin Yasin Ozturk and Emanuele Zappa
Information 2025, 16(6), 448; https://doi.org/10.3390/info16060448 - 27 May 2025
Viewed by 518
Abstract
Structural cracks can critically undermine infrastructure integrity, driving the need for precise, scalable inspection methods beyond conventional visual or 2D image-based approaches. This study presents an automated system integrating photogrammetric 3D reconstruction with deep learning to quantify crack dimensions in a spatial context. [...] Read more.
Structural cracks can critically undermine infrastructure integrity, driving the need for precise, scalable inspection methods beyond conventional visual or 2D image-based approaches. This study presents an automated system integrating photogrammetric 3D reconstruction with deep learning to quantify crack dimensions in a spatial context. Multiple images are processed via Agisoft Metashape to generate high-fidelity 3D meshes. Then, a subset of images are automatically selected based on camera orientation and distance, and a deep learning algorithm is applied to detect cracks in 2D images. The detected crack edges are projected onto a 3D mesh, enabling width measurements grounded in the structure’s true geometry rather than perspective-distorted 2D approximations. This methodology addresses the key limitations of traditional methods (parallax, occlusion, and surface curvature errors) and shows how these limitations can be mitigated by spatially anchoring measurements to the 3D model. Laboratory validation confirms the system’s robustness, with controlled tests highlighting the importance of near-orthogonal camera angles and ground sample distance (GSD) thresholds to ensure crack detectability. By synthesizing photogrammetry and a convolutional neural network (CNN), the framework eliminates subjectivity in inspections, enhances safety by reducing manual intervention, and provides engineers with dimensionally accurate data for maintenance decisions. Full article
(This article belongs to the Special Issue Crack Identification Based on Computer Vision)
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