PCA-Based algorithm for unsupervised bridge crack detection

Abdel‐Qader, Ikhlas; Pashaie-Rad, Sarah; Abudayyeh, Osama; Yehia, Sherif

doi:10.1016/j.advengsoft.2006.06.002

Cited by 156 publications

(66 citation statements)

References 6 publications

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“…where w (1) ij are the weights for the first layer and b (1) i is a bias term. The sum in (1.41), ξ i , is the input the the ith node in the mapping layer that consists of a total of M m nodes.…”

Section: Neural Network Approachesmentioning

confidence: 99%

“…Since the first applications of PCA [21], this technique has found its way into a wide range of different application areas, for example signal processing [75], factor analysis [29,44], system identification [77], chemometrics [20,66] and more recently, general data mining [11,70,58] including image processing [17,72] and pattern recognition [47,10], as well as process 2 U. Kruger, J. Zhang, and L. Xie monitoring and quality control [1,82] including multiway [48], multiblock [52] and multiscale [3] extensions. This success is mainly related to the ability of PCA to describe significant information/variation within the recorded data typically by the first few score variables, which simplifies data analysis tasks accordingly.…”

Section: Introductionmentioning

confidence: 99%

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Developments and Applications of Nonlinear Principal Component Analysis – a Review

Krüger

Zhang

Xie

2008

Lecture Notes in Computational Science and Enginee

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Summary.Although linear principal component analysis (PCA) originates from the work of Sylvester [67] and Pearson [51], the development of nonlinear counterparts has only received attention from the 1980s. Work on nonlinear PCA, or NLPCA, can be divided into the utilization of autoassociative neural networks, principal curves and manifolds, kernel approaches or the combination of these approaches. This article reviews existing algorithmic work, shows how a given data set can be examined to determine whether a conceptually more demanding NLPCA model is required and lists developments of NLPCA algorithms. Finally, the paper outlines problem areas and challenges that require future work to mature the NLPCA research field. IntroductionPCA is a data analysis technique that relies on a simple transformation of recorded observation, stored in a vector z ∈ R N , to produce statistically independent score variables, stored in t ∈ R n , n ≤ N :(1.1)Here, P is a transformation matrix, constructed from orthonormal column vectors. Since the first applications of PCA [21], this technique has found its way into a wide range of different application areas, for example signal processing [75], factor analysis [29,44], system identification [77], chemometrics [20,66] and more recently, general data mining [11,70,58] including image processing [17,72] and pattern recognition [47,10], as well as process 2 U. Kruger, J. Zhang, and L. Xie monitoring and quality control [1,82] including multiway [48], multiblock [52] and multiscale [3] extensions. This success is mainly related to the ability of PCA to describe significant information/variation within the recorded data typically by the first few score variables, which simplifies data analysis tasks accordingly. Sylvester [67] formulated the idea behind PCA, in his work the removal of redundancy in bilinear quantics, that are polynomial expressions where the sum of the exponents are of an order greater than 2, and Pearson [51] laid the conceptual basis for PCA by defining lines and planes in a multivariable space that present the closest fit to a given set of points. Hotelling [28] then refined this formulation to that used today. Numerically, PCA is closely related to an eigenvector-eigenvalue decomposition of a data covariance, or correlation matrix and numerical algorithms to obtain this decomposition include the iterative NIPALS algorithm [78], which was defined similarly by Fisher and MacKenzie earlier on [80], and the singular value decomposition. Good overviews concerning PCA are given in Mardia et al. [45], Joliffe [32], Wold et al. [80] and Jackson [30].The aim of this article is to review and examine nonlinear extensions of PCA that have been proposed over the past two decades. This is an important research field, as the application of linear PCA to nonlinear data may be inadequate [49]. The first attempts to present nonlinear PCA extensions include a generalization, utilizing a nonmetric scaling, that produces a nonlinear optimization problem [42] and constructing a curves...

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Section: Neural Network Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Developments and Applications of Nonlinear Principal Component Analysis – a Review

Krüger

Zhang

Xie

2008

Lecture Notes in Computational Science and Enginee

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“…Yamaguchi et al [49] used scalable local percolation-based image processing techniques and they proved to be efficient and accurate even for large surface images [50]. Abdel-Qader et al [51] used a Principle Component Analysis based algorithm to detect cracks on a bridge surface. In this case, the accuracy of results varied with camera pose and distance from where images are taken.…”

Section: Crackingmentioning

confidence: 99%

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

Koch

Georgieva

Kasireddy

et al. 2015

Advanced Engineering Informatics

712

336

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(2015) A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics, 29 (2). pp. 196-210. ISSN 1474-0346 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/31986/1/Manuscript_KochEtAl_2015_accepted.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the Creative Commons Attribution Non-commercial No Derivatives licence and may be reused according to the conditions of the licence. For more details see: http://creativecommons.org/licenses/by-nc-nd/2.5/ A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 Abstract:To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research. Keywords:Computer Vision, Infrastructure, Condition assessment, Defect detection, Infrastructure monitoring Research Highlights: Visual inspection of civil infrastructure is essential for condition assessment.  We focus on concrete bridges, tunnels, underground pipes, and asphalt pavements.  Accordingly, we review the latest computer vision based defect detection methods.  Using computer vision most relevant types of defects can be automatically detected.  Automatic defect properties retrieval and assessment has not been achieved yet . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 ...

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“…The crack detection filters of different sizes had been designed to identify the cracking regions from the inspection images [1]. Principal Component Analysis (PCA)-based algorithm coupled with linear structure modeling was proposed to detect cracks with linear structure in concrete bridge decks with the best detection accuracy of 73% [2]. For detecting concrete cracks in tunnel, a mobile robot system was built to acquire image data with a Charged Couple Device (CCD) camera [3].…”

Section: Introductionmentioning

confidence: 99%

Application of Computer Vision to Crack Detection of Concrete Structure

Su¹

2013

IJET

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Abstract-Most important civil infrastructures are made of concrete, so accurate information by routine inspection is necessary for structure maintenance. Sometimes temporarily erected scaffoldings are needed for infrastructure inspections. Bridge inspection for example, the inspectors must stand on the platform to examine the underside of a bridge, but such a procedure is risky. At present, several inspection systems coupled with Charge Coupled Devices (CCD) cameras have been developed and applied to infrastructure inspections in order to reduce the danger of accidents to the human inspectors. This paper proposes a computer vision technique based on CCD images to attempt to automatically detect cracks in concrete structure. The experimental result indicates that the optimal accuracies of 90% and 84% could be achieved for the training and testing samples, respectively.

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PCA-Based algorithm for unsupervised bridge crack detection

Cited by 156 publications

References 6 publications

Developments and Applications of Nonlinear Principal Component Analysis – a Review

Developments and Applications of Nonlinear Principal Component Analysis – a Review

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

Application of Computer Vision to Crack Detection of Concrete Structure

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