Tiled fuzzy Hough transform for crack detection

Vaheesan, Kanapathippillai; Chandrakumar, Chanjief; Mathavan, Senthan; Kamal, Khurram; Rahman, Mujib; Al‐Habaibeh, Amin

doi:10.1117/12.2182913

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…7,8 This paper presents a methodology for crack detection by the use of the fuzzy Hough transform. The work extends the earlier work of Vaheesan et al 9 by a considerable amount. In this regard, the line detection capability of the Hough transform is utilized.…”

Section: Introductionsupporting

confidence: 85%

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Detection of pavement cracks using tiled fuzzy Hough transform

Mathavan

Vaheesan

Kumar³

et al. 2017

J. Electron. Imaging

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Surface cracks can be the bellwether of the failure of a road. Hence, 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 generally difficult. Moreover, shallow cracks are very low contrast, making their detection difficult. Therefore, studies on pavement crack detection are active even after years of research. The fuzzy Hough transform is employed, for the first time, to detect cracks from pavement images. A careful consideration is given to the fact that cracks consist of near straight segments embedded in a surface of considerable texture. In this regard, the fuzzy part of the algorithm tackles the segments that are not perfectly straight. Moreover, tiled detection helps reduce the contribution of texture and noise pixels to the accumulator array. The proposed algorithm is compared against a state-of-the-art algorithm for a number of crack datasets, demonstrating its strengths. Precision and recall values of more than 75% are obtained, on different image sets of varying textures and other effects, captured by industrial pavement imagers. The paper also recommends numerical values for parameters used in the proposed method.

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Section: Introductionsupporting

confidence: 85%

“…Using Eqs. (9) and (10), this detection corresponds to a precision and recall of 81.4% and 78.1%, respectively. From Eq.…”

Section: Resultsmentioning

confidence: 98%

Detection of pavement cracks using tiled fuzzy Hough transform

Mathavan

Vaheesan

Kumar³

et al. 2017

J. Electron. Imaging

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“…Conversely, others used correlation techniques by merging images using the digital image correlation (DIC) (Christensen et al, 2021;Gehri et al, 2020;Hamrat et al, 2016). Hough transform was employed in some studies to detect cracks in different surfaces (Mathavan et al, 2017;Navaneetha et al, 2014;Vaheesan et al, 2015). In other studies, crack detection using fusion features-based was investigated (Li et al, 2022(Li et al, , 2019.…”

Section: About Here>mentioning

confidence: 99%

An automatic image processing based on Hough transform algorithm for pavement crack detection and classification

Matarneh

Elghaish²,

Al-Ghraibah³

et al. 2023

SASBE

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PurposeIncipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.Design/methodology/approachThe literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.FindingsAnalysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.Research limitations/implicationsThe outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.Originality/valueHough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.

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“…Introducing fuzzy concept into HT is arisen due to the lines that are not straight in the image domain [19]. In other words, traditional HT only considers the pixels that align on a straight line and non-straight lines detection with HT becomes challenging.…”

Section: Fuzzy Hough Transformmentioning

confidence: 99%

Neutrosophic Hough Transform

Budak

Guo

Şengür

et al. 2017

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Hough transform (HT) is a useful tool for both pattern recognition and image processing communities. In the view of pattern recognition, it can extract unique features for description of various shapes, such as lines, circles, ellipses, and etc. In the view of image processing, a dozen of applications can be handled with HT, such as lane detection for autonomous cars, blood cell detection in microscope images, and so on. As HT is a straight forward shape detector in a given image, its shape detection ability is low in noisy images. To alleviate its weakness on noisy images and improve its shape detection performance, in this paper, we proposed neutrosophic Hough transform (NHT). As it was proved earlier, neutrosophy theory based image processing applications were successful in noisy environments. To this end, the Hough space is initially transferred into the NS domain by calculating the NS membership triples (T, I, and F). An indeterminacy filtering is constructed where the neighborhood information is used in order to remove the indeterminacy in the spatial neighborhood of neutrosophic Hough space. The potential peaks are detected based on thresholding on the neutrosophic Hough space, and these peak locations are then used to detect the lines in the image domain. Extensive experiments on noisy and noise-free images are performed in order to show the efficiency of the proposed NHT algorithm. We also compared our proposed NHT with traditional HT and fuzzy HT methods on variety of images. The obtained results showed the efficiency of the proposed NHT on noisy images.

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Tiled fuzzy Hough transform for crack detection

Cited by 7 publications

References 9 publications

Detection of pavement cracks using tiled fuzzy Hough transform

Detection of pavement cracks using tiled fuzzy Hough transform

An automatic image processing based on Hough transform algorithm for pavement crack detection and classification

Neutrosophic Hough Transform

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