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Journal ArticleDOI

Computer Vision Techniques for Automatic Structural Assessment of Underground Pipes

TL;DR: In this paper, a system for the automatic assessment of the structural condition of underground pipes is presented, which consists of image preprocessing, a sequence of morphological operations to accurately extract pipe joints and laterals (where smaller pipe is connected to main bigger pipe), and statistical filters for detecting surface cracks in the pipeline network.
Abstract: Pipeline surface defects such as cracks cause major problems for asset managers, particularly when the pipe is buried under the ground. The manual inspection of surface defects in the underground pipes has a number of drawbacks, including subjectivity, varying standards, and high costs. An automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer asset managers an opportunity to significantly improve quality and reduce costs. This article presents a system for the application of computer vision techniques to the automatic assessment of the structural condition of underground pipes. The algorithm consists of image preprocessing, a sequence of morphological operations to accurately extract pipe joints and laterals (where smaller pipe is connected to main bigger pipe), and statistical filters for detection of surface cracks in the pipeline network. The proposed approach can be completely automated and has been tested on over 1,000 scanned images of underground pipes from major cities in North America.
Citations
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Journal ArticleDOI
TL;DR: A framework for quasi real-time damage detection on video using the trained networks is developed and the robustness of the trained Faster R-CNN is evaluated and demonstrated using 11 new 6,000 × 4,000-pixel images taken of different structures.
Abstract: Computer vision-based techniques were developed to overcome the limitations of visual inspection by trained human resources and to detect structural damage in images remotely, but most methods detect only specific types of damage, such as concrete or steel cracks. To provide quasi real-time simultaneous detection of multiple types of damages, a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based structural visual inspection method is proposed. To realize this, a database including 2,366 images (with 500 × 375 pixels) labeled for five types of damages—concrete crack, steel corrosion with two levels (medium and high), bolt corrosion, and steel delamination—is developed. Then, the architecture of the Faster R-CNN is modified, trained, validated, and tested using this database. Results show 90.6%, 83.4%, 82.1%, 98.1%, and 84.7% average precision (AP) ratings for the five damage types, respectively, with a mean AP of 87.8%. The robustness of the trained Faster R-CNN is evaluated and demonstrated using 11 new 6,000 × 4,000-pixel images taken of different structures. Its performance is also compared to that of the traditional CNN-based method. Considering that the proposed method provides a remarkably fast test speed (0.03 seconds per image with 500 × 375 resolution), a framework for quasi real-time damage detection on video using the trained networks is developed.

849 citations


Cites background from "Computer Vision Techniques for Auto..."

  • ..., 2014), underground concrete pipe cracks (Sinha et al., 2003), and potholes in asphalt pavement (Koch and Brilakis, 2011)....

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  • ...DOI: 10.1111/mice.12334 cracks (Ying and Salari, 2010; Cord and Chambon, 2012; Zalama et al., 2014), underground concrete pipe cracks (Sinha et al., 2003), and potholes in asphalt pavement (Koch and Brilakis, 2011)....

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Journal ArticleDOI
TL;DR: In this paper, an integrated model consisting of crack quantification, change detection, neural networks, and 3D visualization models to visualize the defects in such a way that it mimics the on-site visual inspections is presented.

268 citations

Journal ArticleDOI
01 Feb 2013
TL;DR: A contact-less remote-sensing crack detection and quantification methodology based on 3D scene reconstruction (computer vision), image processing, and pattern recognition concepts is introduced, giving a robotic inspection system the ability to analyze images captured from any distance and using any focal length or resolution.
Abstract: Visual inspection of structures is a highly qualitative method in which inspectors visually assess a structure’s condition. If a region is inaccessible, binoculars must be used to detect and characterize defects. Although several Non-Destructive Testing methods have been proposed for inspection purposes, they are nonadaptive and cannot quantify crack thickness reliably. In this paper, a contact-less remote-sensing crack detection and quantification methodology based on 3D scene reconstruction (computer vision), image processing, and pattern recognition concepts is introduced. The proposed approach utilizes depth perception to detect cracks and quantify their thickness, thereby giving a robotic inspection system the ability to analyze images captured from any distance and using any focal length or resolution. This unique adaptive feature is especially useful for incorporating mobile systems, such as unmanned aerial vehicles, into structural inspection methods since it would allow inaccessible regions to be properly inspected for cracks. Guidelines are presented for optimizing the acquisition and processing of images, thereby enhancing the quality and reliability of the damage detection approach and allowing the capture of even the slightest cracks (e.g., detection of 0.1 mm cracks from a distance of 20 m), which are routinely encountered in realistic field applications where the camera-object distance and image contrast are not controllable.

226 citations

Journal ArticleDOI
TL;DR: A vision-based crack detection methodology is introduced that extracts the whole crack from its background, where the regular edge-based approaches just segment the crack edges, appropriate for the development of a crack thickness quantification system.

199 citations

Journal ArticleDOI
TL;DR: A survey and evaluation of relevant studies that appear promising and practical for this purpose is presented in this paper, where several image processing techniques, including enhancement, noise removal, registratio, etc., are evaluated.
Abstract: Automatic health monitoring and maintenance of civil infrastructure systems is a challenging area of research. Nondestructive evaluation techniques, such as digital image processing, are innovative approaches for structural health monitoring. Current structure inspection standards require an inspector to travel to the structure site and visually assess the structure conditions. A less time consuming and inexpensive alternative to current monitoring methods is to use a robotic system that could inspect structures more frequently. Among several possible techniques is the use of optical instrumentation (e.g. digital cameras) that relies on image processing. The feasibility of using image processing techniques to detect deterioration in structures has been acknowledged by leading experts in the field. A survey and evaluation of relevant studies that appear promising and practical for this purpose is presented in this study. Several image processing techniques, including enhancement, noise removal, registratio...

147 citations


Cites background or methods from "Computer Vision Techniques for Auto..."

  • ...In automatic classification of patterns or objects in an image, the spectral and textural attributes are used as features (Sinha et al. 2003)....

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  • ...The algorithm proposed by Sinha et al. (2003) consists of image processing, segmentation, feature extraction, pattern recognition and a proposed neuro-fuzzy network for classification....

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  • ...…1999, Rey et al. 2002, Bosc et al. 2003), underwater inspections (Lebart et al. 2000, Edgington et al. 2003), transportation systems (Achler and Trivedi 2004) and nondestructive structural health monitoring (Dudziak et al. 1999, Abdel-Qader et al. 2003, Sinha et al. 2003, Poudel et al. 2005)....

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  • ...…vector dimension, it is possible to map the principal features of a pattern from a higher dimensional space to a lower dimensional space by means of a mapping transformation, such as discrete cosine transformation, Fourier transformation or PCA (Karhunen–Loeve transform) (Sinha et al. 2003)....

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  • ...A study on using computer vision techniques for automatic structural assessment of underground pipes has been carried out by Sinha et al. (2003)....

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References
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Book
01 Aug 1996
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Abstract: A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.

52,705 citations

Journal ArticleDOI
01 Nov 1973
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

20,442 citations


"Computer Vision Techniques for Auto..." refers background or methods in this paper

  • ..., textural features) distinguish objects by using statistical measures based on gray-scale co-occurrence matrix (Haralick, 1973) and its variant, such as gray-scale difference vector, moment invariants, and gray-scale difference matrix....

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  • ...Transform coefficient feature extraction has proved practical in several applications in which the transform domain features are used as inputs to a pattern recognition classification system (Haralick, 1973; Shaikh and Tian, 1996)....

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  • ...Textural features are those characteristics such as smoothness, fineness, coarseness, or a particular pattern associated with an image (Haralick, 1973)....

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  • ...The second categories (i.e., textural features) distinguish objects by using statistical measures based on gray-scale co-occurrence matrix (Haralick, 1973) and its variant, such as gray-scale difference vector, moment invariants, and gray-scale difference matrix....

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Book
01 Jan 1973
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Abstract: Provides a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition. The topics treated include Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.

13,647 citations