3D reconstruction of surface cracks using bi-frequency eddy current images and a direct semi-analytic model

Cai, Caifang; Boré, Thierry; Delaine, Florentin; Gasnier, Nicolas; Vourch, E.

doi:10.1088/1742-6596/904/1/012019

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Additionally, there are many other techniques that utilise ultrasounds [25][26][27], X-rays [28], and Eddy Current (EC) [29]. These papers reconstructed images into 3D (three-dimensional) space [30,31].…”

Section: Literature Surveymentioning

confidence: 99%

A Deep Learning- Image Based Approach for Detecting Cracks in Buildings

Jayaraju¹,

Somasundaram²,

Suprakash³

et al. 2022

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Buildings expand and contract in response to their environment, which results in cracks in the structure. This can pose a serious threat to the people who use it, and these movements are frequently too small to be observed, and thus go unnoticed. Cracks can be caused by a variety of factors, including defects in the construction process, ground movement, foundation failure, and decay of the building fabric. If a structure is unable to accommodate this movement, cracking is likely to occur, posing a serious risk to the building's structural integrity. Only after cracks are identified can they be treated, and existing manual methods of sketching the crack patterns are highly subjective to the person performing the analysis, are frequently constrained by high costs, equipment and tool availability, and are extremely time consuming. In this paper, 40,000 images divided into two and categorized into positive and negative cracks are used as input and the presence of cracks is detected using a deep learning technique. The following crack types are included in the experimentation: hairline, stepped, vertical, and horizontal. In comparison to conventional image processing and other deep learning-based techniques, the proposed Convolutional Neural Network (CNN) achieves significantly higher accuracy than the Recurrent Neural Network (RNN). This paper’s objective is to create a model which can detect the cracks through deep learning methodology, and this will be the innovative region in crack detection using neural net framework.

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Section: Literature Surveymentioning

confidence: 99%

A Deep Learning- Image Based Approach for Detecting Cracks in Buildings

Jayaraju¹,

Somasundaram²,

Suprakash³

et al. 2022

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“…Hence, the demands of measuring the geometrical parameters of the cracks accurately are ever increasing [4][5][6][7][8], and the number of articles for crack measurements has also been increased [9][10][11][12][13][14][15][16][17]. In this process, many methods such as using ultrasounds [18][19][20][21], X-rays [22], and eddy current (EC) [23] sources are developed, and the images are reconstructed in 3D (three-dimensional) [24] form. Hence, it is considered that these methods have good prospects for practical use in civil engineering [25].…”

Section: Introductionmentioning

confidence: 99%

Determination of Geometric Parameters of Cracks in Concrete by Image Processing

Vashpanov

Son

Heo

et al. 2019

Advances in Civil Engineering

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The 8-bit RGB image of a cracked concrete surface, obtained with a high-resolution camera based on a close-distance photographing and using an optical microscope, is used to estimate the geometrical parameters of the crack. The parameters such as the crack’s width, depth, and morphology can be determined by the pixel intensity distribution of the image. For the estimation, the image is transformed into 16-bit gray scale to enhance the geometrical parameters of the crack and then a mathematical relationship relating the intensity distribution with the depth and width is derived based on the enhanced image. This relationship enables to estimate the width and depth with ±10% and ±15% accuracy, respectively, for the crack samples used for the experiments. It is expected that the accuracy can be further improved if the 8-bit RGB image is synthesized by the images of the cracks obtained with different illumination directions.

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3D reconstruction of surface cracks using bi-frequency eddy current images and a direct semi-analytic model

Cited by 2 publications

References 8 publications

A Deep Learning- Image Based Approach for Detecting Cracks in Buildings

A Deep Learning- Image Based Approach for Detecting Cracks in Buildings

Determination of Geometric Parameters of Cracks in Concrete by Image Processing

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