Surface Distresses Detection of Pavement Based on Digital Image Processing

Ouyang, Aiguo; Luo, Chagen; Zhou, Chao

doi:10.1007/978-3-642-18369-0_42

Cited by 24 publications

(22 citation statements)

References 6 publications

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“…The typical pavement disease detection methods are: edge detection method [2], morphology method [3,4], region growing method [5] , texture analysis [6], etc. In recent years, there also appears some new methods, such as fuzzy set method [7] and pavement disease method base on neural network [8].…”

Section: Pavement Detection Methods Based On Image Processingmentioning

confidence: 99%

“…If the value of ε is closer to the fractal feature of measured object, the measurement value of fractal dimension is more accurate. Let ε'=ε×[(floor{max(f(ε)) -min(f(ε))}+ 1)/256] (2) Here ( ) f ε shows gray-scale of corresponding point within the ε ε × area. Max and min denote operation of maximum and minimum.…”

Section: Fractal Dimension Calculation Of Gray-scale Imagementioning

confidence: 99%

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Fractal dimension applied in highway surface crack detection

Chen

Cao

2012

SPIE Proceedings

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This paper presents a fractal dimension calculation technique of highway pavement which is used to detect the pavement cracking. Firstly, the impulse noises generated by pavement unevenness are eliminated by the use of median filtering of pavement image. Secondly, the fractal dimension of fissure is calculated according to the crack's fractal feature. Finally, the pavement damage is detected and located with respect to fractal dimension. These three steps can realize the location detection and damage rate computation of pavement damage successfully. To a number of random images, the experiment results show that the fractal dimension of pavement crack region is from 2.25 to 2.99 basically.

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Section: Pavement Detection Methods Based On Image Processingmentioning

confidence: 99%

Section: Fractal Dimension Calculation Of Gray-scale Imagementioning

confidence: 99%

Fractal dimension applied in highway surface crack detection

Chen

Cao

2012

SPIE Proceedings

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“…To detect the pavement distresses on images, Ouyang et al tried an approach based on filtering the images to remove the background, or pavement texture, and image enhancements, segmentation and Canny edge detection [5].…”

Section: Figure 1 -A Line-scan Camera-laser Illuminator Device On a Pmentioning

confidence: 99%

Hybrid Human-Artificial Intelligence Approach for Pavement Distress Assessment (Picucha)

Salini¹,

Xu²,

Carvalho³

2017

CEJ

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The pavement surface condition assessment is a critical component for a proper pavement management system as well as for pavement rehabilitation design. A number of devices were developed to automatically record surface distresses in a continuous survey mode, but the software required for automatic distress identification remains a big challenge. In this study, a new method named PICture Unsupervised Classification with Human Analysis (PICUCHA) is proposed to circumvent many of the limitations of existing approaches, based on a combination of human and artificial intelligence. It was designed from scratch to be capable to identify sealed and unsealed cracks, potholes, patches, different types of pavements and others. The self-learning algorithms do not use any distresses predefinition and can process images taken by cameras with different brands, technologies and resolution. This study describes some key aspects of the new method and provides examples in which PICUCHA was tested in real conditions showing accuracy up to 96.9% in image pattern detection and classification.

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“…Two primary segmentation methods utilized for pavement distress identification are edge-detection and thresholding [100,115,127]. [61,101] applied edge-detection algorithms such as Canny and fast Haar tranforms for crack identification [17]. [68] utilized image segmentation and texture analysis to identify potholes.…”

Section: Visiblementioning

confidence: 99%

Review of remote sensing methodologies for pavement management and assessment

Schnebele

Tanyu

Cervone

et al. 2015

Eur. Transp. Res. Rev.

165

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Introduction Evaluating the condition of transportation infrastructure is an expensive, labor intensive, and time consuming process. Many traditional road evaluation methods utilize measurements taken in situ along with visual examinations and interpretations. The measurement of damage and deterioration is often qualitative and limited to point observations. Remote sensing techniques offer nondestructive methods for road condition assessment with large spatial coverage. These tools provide an opportunity for frequent, comprehensive, and quantitative surveys of transportation infrastructure. Methods The goal of this paper is to provide a bridge between traditional procedures for road evaluation and remote sensing methodologies by creating a comprehensive reference for geotechnical engineers and remote sensing experts alike. Results A comprehensive literature review and survey of current techniques and research methods is provided to facilitate this bridge. A special emphasis is given to the E. Schnebele ( ) · G. Cervone · N. Waters

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Surface Distresses Detection of Pavement Based on Digital Image Processing

Cited by 24 publications

References 6 publications

Fractal dimension applied in highway surface crack detection

Fractal dimension applied in highway surface crack detection

Hybrid Human-Artificial Intelligence Approach for Pavement Distress Assessment (Picucha)

Review of remote sensing methodologies for pavement management and assessment

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