Novel Approach to Pavement Cracking Detection Based on Fuzzy Set Theory

Cheng, Heng-Da; Chen, Jim-Rong; Glazier, Chris; Hu, Yinlong

doi:10.1061/(asce)0887-3801(1999)13:4(270)

Cited by 169 publications

(71 citation statements)

References 16 publications

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“…Furthermore, the authors have proposed a classification method whereby cracks are classified as alligator, transverse and longitudinal. Cheng et.al, in their research [5], proposed a novel technique to pavement crack detection using Fuzzy Set Theory. They compared the darkness of the pixels with the surroundings and mapped the fuzzified images to the crack domain.…”

Section: Literature Reviewmentioning

confidence: 99%

Tiled fuzzy Hough transform for crack detection

Vaheesan¹,

Chandrakumar

Mathavan

et al. 2015

Twelfth International Conference on Quality Control by Artificial Vision 2015

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Surface cracks can be the bellwether of the failure of any component under loading as it indicates the component's fracture due to stresses and usage. For this reason, crack detection is indispensable for the condition monitoring and quality control of road surfaces. Pavement images have high levels of intensity variation and texture content, hence the crack detection is difficult. Moreover, shallow cracks result in very low contrast image pixels making their detection difficult. For these reasons, studies on pavement crack detection is active even after years of research. In this paper, the fuzzy Hough transform is employed, for the first time to detect cracks on any surface. The contribution of texture pixels to the accumulator array is reduced by using the tiled version of the Hough transform. Precision values of 78% and a recall of 72% are obtaining for an image set obtained from an industrial imaging system containing very low contrast cracking. When only high contrast crack segments are considered the values move to mid to high 90%.

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Section: Literature Reviewmentioning

confidence: 99%

Tiled fuzzy Hough transform for crack detection

Vaheesan¹,

Chandrakumar

Mathavan

et al. 2015

Twelfth International Conference on Quality Control by Artificial Vision 2015

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“…Thresholding is frequently used to segment cracks, fixed threshold in [6][7] or fuzzy threshold in [8]. Some methods [9,10] divide image into grid cells and then classify each cell as crack or crack-free cell by comparing mean and standard deviation of the cell with their neighbors or by UINTA filtering [11].…”

Section: Review Of Defect Detection Methodsmentioning

confidence: 99%

Free-form anisotropy: A new method for crack detection on pavement surface images

Nguyen

Bégot

Duculty

et al. 2011

2011 18th IEEE International Conference on Image Processing

106

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This paper presents a new measure which takes into accounts simultaneously brightness and connectivity, in the segmentation step, for crack detection on road pavement images. Features which are calculated along every free-form paths provide detection of cracks with any form and any orientation. The method proposed does not need learning stage of free defect texture to perform default detection. Experimental results were conducted on some samples of different kinds of pavements. Results of the method are also given on other kinds of images and can provide perspectives on other domains as road extraction on satellite images or segment blood vessels in retinal images.

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“…Pre-processing Contrast stretching [1,7] Equalization histogram [3,6] Road image segmentation Fixed threshold [1,[8][9][10][11] Fuzzy entropy threshold [6,7] Wavelet transform and threshold on the space of coefficient [ Most of these existing methods, only use local feature of crack to segment images. Crack pixels are considered as darker pixels or as local extrema, but connectivity and orientation of crack were not considered in the segmentation process.…”

Section: Fig 1 Some Examples Of Road Defects: Longitudinal Crack (Amentioning

confidence: 99%

Pavement Cracking Detection using an Anisotropy Measurement

Nguyen

Bégot

Duculty

et al. 2010

Computer Graphics and Imaging

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Automatic pavement cracking detection is a part of road maintenance and rehabilitation strategies. Cracks detection is one of the main features used by road authorities to manage efficiently its networks. Different systems are available to perform road analysis. We give a short description of some of them. Apparatus which was used to provide our images is described with more details. Road surface is made using randomly organized aggregates which can have different sizes. Scanned pictures of theses surfaces appear as random distribution of a reduced set of gray levels. Automatic crack detection is a difficult task due to the noisy pavement surface. In this paper, we introduce a measure of anisotropy for the characterization of cracks. The basic idea of this method is to detect the variation of features by considering different orientations. Noise variation and defect properties can be take into account by our method. Comparative results of anisotropy method with threshold method and 2D wavelet transform method are presented to illustrate benefits of anisotropy. We show that this method can be used to detect others types of defects, such as joints.

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Novel Approach to Pavement Cracking Detection Based on Fuzzy Set Theory

Cited by 169 publications

References 16 publications

Tiled fuzzy Hough transform for crack detection

Tiled fuzzy Hough transform for crack detection

Free-form anisotropy: A new method for crack detection on pavement surface images

Pavement Cracking Detection using an Anisotropy Measurement

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