Comparative analysis of image binarization methods for crack identification in concrete structures

Kim, Hyun-Jun; Ahn, Eunjong; Cho, Su Jin; Shin, Myoungsu; Sim, Sung‐Han

doi:10.1016/j.cemconres.2017.04.018

Cited by 165 publications

(92 citation statements)

References 21 publications

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“…As pointed out by Kim et al [9], these methods have certain disadvantages. e Bernsen method is found to require much more computational cost than the other methods; it is because this approach needs to compute an additional histogram in deciding threshold values.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…It is because texts and cracks have similar properties, and they feature distinguishable lines and curves. Nevertheless, crack detection utilizing the Otsu method [15] as the standard image binarization approach is unsatisfying; the reason is that image binarization depends on the image quality, characteristics of the background surface, and associated parameters [9]. Crack detection also suffers from challenges such as low contrast, uneven illumination, noise pollution, and existence of shading, blemishes, or concrete spall in images [11].…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, fast and reliable surface crack detection and analysis by means of automatic procedure is highly useful to replace the slow and subjective inspection of human inspectors [9]. Recent reviews done in [7,[9][10][11] pointed out an increasing trend of applying image processing technique for boosting the productivity of detecting crack in structures.…”

Section: Introductionmentioning

confidence: 99%

“…Recent reviews done in [7,[9][10][11] pointed out an increasing trend of applying image processing technique for boosting the productivity of detecting crack in structures.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding

Hoang

2018

Advances in Civil Engineering

114

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e detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. e manual process of crack detection is painstakingly time-consuming and suffers from subjective judgments of inspectors. is study establishes an intelligent model based on image processing techniques for automatic crack recognition and analyses. In the new model, a gray intensity adjustment method, called Min-Max Gray Level Discrimination (M2GLD), is proposed to preprocess the image thresholded by the Otsu method. e goal of this gray intensity adjustment method is to meliorate the accuracy of the crack detection results. Experimental results point out that the integration of M2GLD and the Otsu method, followed by other shape analysis algorithms, can successfully detect crack defects in digital images. erefore, the constructed model can be a useful tool for building management agencies and construction engineers in the task of structure maintenance.

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Section: Review Of Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Recent reviews done in [7,[9][10][11] pointed out an increasing trend of applying image processing technique for boosting the productivity of detecting crack in structures.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding

Hoang

2018

Advances in Civil Engineering

114

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“…Alam et al used Digital Image Correlation (DIC) together to an acoustic emission technique to measure crack opening and spacing in beams with different cross section under bending moment. Kim el al . studied the influence of different threshold in the binarized images to best approach the width of cracks.…”

Section: Introductionmentioning

confidence: 99%

A method to measure small local strains in concrete surfaces using its natural texture and image cross‐correlation

Espinosa

Mas

2019

Struct Control Health Monit

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Summary This paper proposes a method to measure the displacement of every point on a concrete surface using its original surface texture and digital image cross‐correlation (DICC). The main advantage is that it does not need to add or attach any particular pattern or target to the surface, contrary to the current methods based in DICC. The method is tested in a concrete probe during loading–unloading cycles under its yield limit, with no cracks appearing in the surface. The total surface of the probe was tessellated in optimal local areas, and DICC with peak refinement was performed on each local area. In order to find the optimal size for that areas, we propose here a method based on the image local entropy. We show that, with natural nonoptimized textures, the proposed technique allows to locally measure the concrete deformation due to small strains (<0.1%) with an error below 50 με. Although results are more inaccurate than those obtained by a strain gauge, our method is able to noninvasively determine the strains in a concrete surface under small loads with reasonable accuracy, thus allowing its use in unreachable or historical structures where no attachment or target painting is possible.

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Comparison of threshold algorithms for automatic processing of fat crystal microscopic images based on ImageJ

Xiong,

Qi,

Zhang

2024

J Americ Oil Chem Soc

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Microscopic image analysis is a crucial tool in fat crystallization research, enabling the analysis of crystal size, network structure, fractal dimension and other parameters through binarization. It is essential to seek an appropriate thresholding algorithm to binarize fat crystal images, which plays a vital role in image segmentation. In this article, the effectiveness of 17 thresholding algorithms such as Default, Mean, IsoData, Otsu, Li and Triangle were analyzed in processing fat crystal images with different shapes, background colors and image intensities. This was expected to discover a stable and objective thresholding algorithm for the binarization of fat crystal images. The performance evaluation was conducted according to the peak signal noise ratio (PSNR), structural similarity index (SSIM) and region non‐uniformity (RNU) parameter. Moreover, the comparative analysis of crystal size error, crystal area fraction and intraclass correlation coefficients (ICC) for fractal dimension values would provide a foundation for the selection of thresholding techniques for fat crystal network images. The results indicated that the Default algorithm exhibited remarkable robustness and applicability with high‐quality and stable outputs in fat crystal image processing.

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Comparative analysis of image binarization methods for crack identification in concrete structures

Cited by 165 publications

References 21 publications

Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding

Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding

A method to measure small local strains in concrete surfaces using its natural texture and image cross‐correlation

Comparison of threshold algorithms for automatic processing of fat crystal microscopic images based on ImageJ

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