A Neural Network based Technique for Automatic Classification of Road Cracks

Bray, Joe; Verma, Brijesh; Li, Xue; He, Wade

doi:10.1109/ijcnn.2006.246782

Cited by 39 publications

(23 citation statements)

References 9 publications

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“…In recent years, a number of approaches for pavement crack classification have been proposed which generally fall into two categories-the supervised and the unsupervised. The former includes a series of neural network-based approaches [8][9][10][13][14][15][16][17][18][19], while the later are rule-based approaches [1,[20][21][22]. Among the neural network-based approaches, Kaseko et al [13] exploited a two-stage piecewise linear neural network for crack classification and proved that it outperforms the Bayes classifier and the k-nearest neighbor (k-NN) classifier.…”

Section: Previous Workmentioning

confidence: 99%

“…Though a variety of approaches for pavement crack classification have been proposed in the last two decades, most of them cannot meet the requirements in practice due to their inadequate consideration on spatial distribution features of the cracks. For example, the projection histogram methods [8][9][10] can be qualified to identify the directional difference between cracks, but it may not be capable of distinguishing the density difference. In a pavement image, typically, a crack has a linear or curvilinear structure, the spatial distribution of the crack points determines which type of crack it is.…”

Section: Introductionmentioning

confidence: 99%

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Pavement Crack Classification via Spatial Distribution Features

Zou

Liu

2011

EURASIP J. Adv. Signal Process.

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Pavement crack types provide important information for making pavement maintenance strategies. This paper proposes an automatic pavement crack classification approach, exploiting the spatial distribution features (i.e., direction feature and density feature) of the cracks under a neural network model. In this approach, a direction coding (D-Coding) algorithm is presented to encode the crack subsections and extract the direction features, and a Delaunay Triangulation technique is employed to analyze the crack region structure and extract the density features. As regarding skeletonized crack sections rather than crack pixels, the spatial distribution features hold considerable feature significance for each type of cracks. Empirical study indicates a classification precision of over 98% of the proposed approach.

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Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pavement Crack Classification via Spatial Distribution Features

Zou

Liu

2011

EURASIP J. Adv. Signal Process.

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“…The previous discussion determined that the potholes have known by two features that are texture and shape features. The authors also used many methods to identify it such as Support Vector Machine (Li, Hou, Yang, & Dong, 2009), Artificial Neural Network (Bray, Verma, Li, & He, 2006;Xu, Ma, Liu, & Niu, 2008), and Fuzzy method (Ouma, Opudo, & Nyambenya, 2015). There are many precisions for the experiments.…”

Section: Introductionmentioning

confidence: 99%

Potholes Road Classification by Shape and Area Features

Rosita

Sugianto

2019

RLJ

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Background of the study: Generally, during the rainy season, many potholes asphalt road are found. The high rainfall results in the fragile contour of the asphalt road and triggers a traffic accident. In the last decade, the development of potholes asphalt road detection has various method approaches.Purpose: The research used precision to get a performance of the system.Method: In this study, the development system can classify potholes asphalt road by a simple algorithm. It also considers the time and space complexity.Findings: The algorithms as possible and only uses the handy-camera device to capture data which the level of performance as good as the results of previous research. Capturing data is also various distances with 450 point angles. For classification steps, the system applied two main features, area and shape feature of the object. The used parameters for these features are the length of major and minor axis object. It used to calculate area and eccentricity values.Conclusion: In conclusion, the experiment result reaches 81.696% of the 1125 frames used.

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“…This was followed by who introduced the technique in analyzing cracks in sewer pipes and road pavements. In addition, neural network technique was developed by Bray et al (2006) for road cracks classification automatically. However, both methods used existing methods, such as histogram equalization (HE) and global thresholding, before sending the information to the network.…”

Section: Introductionmentioning

confidence: 99%

A Contrast Stretching Bilateral Closing Top‐Hat Otsu Threshold Technique for Crack Detection in Images

et al. 2012

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Summary: Detection of cracks from stainless steel pipe images is done using contrast stretching technique. The technique is based on an image filter technique through mathematical morphology that can expose the cracks. The cracks are highlighted and noise removal is done efficiently while still retaining the edges. An automated crack detection system with a camera platform has been successfully implemented. We compare crack extraction in terms of quality measures with those of Otsu's threshold technique and the another technique (Iyer and Sinha, 2005). The algorithm shown is able to achieve good results and perform better than these other techniques. SCANNING 00: 1-13, 2012. C 2012 Wiley Periodicals, Inc.

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A Neural Network based Technique for Automatic Classification of Road Cracks

Cited by 39 publications

References 9 publications

Pavement Crack Classification via Spatial Distribution Features

Pavement Crack Classification via Spatial Distribution Features

Potholes Road Classification by Shape and Area Features

A Contrast Stretching Bilateral Closing Top‐Hat Otsu Threshold Technique for Crack Detection in Images

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