Introduction of a wavelet transform based on 2D matched filter in a Markov random field for fine structure extraction: application on road crack detection

Chambon, Sylvie; Subirats, Peggy; Dumoulin, Jean

doi:10.1117/12.805437

Cited by 38 publications

(34 citation statements)

References 14 publications

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“…Training artificial neural networks for pavement distress image compression, noise reduction and evaluation led to further studies and activities on the same technique [7][8]. Chambon et al [9] introduced a wavelet and 2D matched filter in order to define an adapted wavelet and then used the results of this multi scale detection into a Markov Random Field (MRF) process to segment fine structures of images for road crack detection.…”

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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“…According to [5], in most of existing methods, classification step is trivial due to the easy task consisting in separating different crack types (longitudinal, transversal and alligator).…”

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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“…One method proposed by Sylvie Chambon et al [3] introduced a 2D matched filter in order to define an adapted mother wavelet and then to use the result of this multi-scale detection into a Markov Random Field (MRF) process to segment fine structures of the image. Fine structure extraction has many advantages in different domains of image processing.…”

Section: Related Workmentioning

confidence: 99%

Automatic Road Distress Detection and Analysis

Daniel¹,

Preeja²

2014

IJCA

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Pavement distresses (damages) are important information for evaluating the road condition and conducting the necessary road maintenance activities. Conventional human visual pavement distress detection method is time consuming, very expensive, labour-intensive and dangerous due to exposure to traffic. Numbers of methods have been introduced for road damage detection in the context of fine structure extraction. Due to the unceasing traffic increase, the automation of pavement surface distress monitoring is more and more required. Several techniques are developed for this purpose. But all those approaches access the road condition based on cracks on roads. Due to the various climatic factors, Indian roads suffer from some other types of distresses like potholes also. The goal of this paper is to introduce a novel technique to determine the road condition based on the cracks and potholes on road surfaces. Thus a fully integrated system is proposed for the automatic detection and characterization of distresses in road and flexible pavement surfaces and to detect its severity. The main tasks involved are Collection of images, Distress Detection and Classification using Supervised training approach, Assignment of crack's severity levels to analyze the road performance.

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Introduction of a wavelet transform based on 2D matched filter in a Markov random field for fine structure extraction: application on road crack detection

Cited by 38 publications

References 14 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

Automatic Road Distress Detection and Analysis

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