Deep learning algorithm for real-time automatic crack detection, segmentation, qualification

Xu, Gang; Yue, Qingrui; Liu, Xiaogang

doi:10.1016/j.engappai.2023.107085

Cited by 17 publications

(1 citation statement)

References 40 publications

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“…The final stage of failure development of bodies, which is the subject of fracture mechanics investigation, is crack propagation (unstable or stable). Attention is paid to understanding this behaviour of structural materials to the deterministic approaches and to the computational modelling of such processes for the case of several representative types of composites, unlike various types of probabilistic considerations by [4][5][6][7], or discrete element techniques by [8,9], or even those relying on techniques of artificial intelligence as [10]. This article used the long-time experience of both authors in this research field, which is confronted with new models, methods and results developed in recent years.…”

Section: Introductionmentioning

confidence: 99%

Use of Cohesive Approaches for Modelling Critical States in Fibre Reinforced Structural Materials

Kozák,

Vala

2024

Preprint

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This paper is showing the results of a study focused on the occurrence of failures in heterogeneous structural materials, especially on the issue of modelling the formation and propagation of cracks. Attention is paid to the application of the finite element method to various types of materials in order to find critical parameters determining the behaviour of materials during the damage process. Application of damage mechanisms and approaches to modelling the origin of crack propagation by modifications in FEM systems are presented. Furthermore, some practical applications are tested. The main effort is devoted to composites and searching for procedures when using cohesion models for their more accurate modelling, especially near the stress concentrators, or before the crack tip. The use of the cohesive approach of elements that represent the physical nature of energy release in front of the crack front has proven to be promising not only in the direct use of cohesive elements, but also in combination with modified methods of standard finite elements. The authors of this article use their long-term experience in the given issue, which they consistently confront with developments in this area in recent years.

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Section: Introductionmentioning

confidence: 99%

Use of Cohesive Approaches for Modelling Critical States in Fibre Reinforced Structural Materials

Kozák,

Vala

2024

Preprint

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Crack detection based on attention mechanism with YOLOv5

Lan,

Yang,

Zhou

et al. 2024

Engineering Reports

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In order to reduce the manual workload and reduce the maintenance cost, it is particularly important to realize automatic detection of cracks. Aiming at the problems of poor real‐time performance and low precision of traditional pavement crack detection, a crack detection method based on improved YOLOv5 one‐step target detection algorithm of convolutional neural network is proposed by using the advantages of depth learning network in target detection. The images were manually marked with LabelImg annotation software, and then the network model parameters were obtained through improving the YOLOv5 network training. Finally, the cracks are verified and detected by the established model. In addition, the precision and speed of crack detection using YOLOv3, YOLOv5s, and YOLOv5s‐attention models are compared by using Precision, Recall, and F1. After comparison, it is found that the detection precision of YOLOv5s‐attention is improved by 1.0%, F1 by 0.9%, and mAP@.5 by 1.8%.

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Advanced Crack Detection in Bidirectional Gradient Material FGM Beams: A Neural Network Approach with Adam Optimization

Moukhliss,

Ayoub,

Rahmouni

et al. 2024

Iran J Sci Technol Trans Mech Eng

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Deep learning algorithm for real-time automatic crack detection, segmentation, qualification

Cited by 17 publications

References 40 publications

Use of Cohesive Approaches for Modelling Critical States in Fibre Reinforced Structural Materials

Use of Cohesive Approaches for Modelling Critical States in Fibre Reinforced Structural Materials

Crack detection based on attention mechanism with YOLOv5

Advanced Crack Detection in Bidirectional Gradient Material FGM Beams: A Neural Network Approach with Adam Optimization

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