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Journal ArticleDOI

Analysis of edge-detection techniques for crack identification in bridges

15 Sep 2003-Journal of Computing in Civil Engineering (American Society of Civil Engineers)-Vol. 17, Iss: 4, pp 255-263
TL;DR: This paper provides a comparison of the effectiveness of four crack-detection techniques: fast Haar transform (FHT), fast Fourier transform, Sobel, and Canny and shows that the FHT was significantly more reliable than the other three edge-detector techniques in identifying cracks.
Abstract: Bridge monitoring and maintenance is an expensive yet essential task in maintaining a safe national transportation infrastructure. Traditional monitoring methods use visual inspection of bridges on a regular basis and often require inspectors to travel to the bridge of concern and determine the deterioration level of the bridge. Automation of this process may result in great monetary savings and can lead to more frequent inspection cycles. One aspect of this automation is the detection of cracks and deterioration of a bridge. This paper provides a comparison of the effectiveness of four crack-detection techniques: fast Haar transform (FHT), fast Fourier transform, Sobel, and Canny. These imaging edge-detection algorithms were implemented in MatLab and simulated using a sample of 50 concrete bridge images (25 with cracks and 25 without). The results show that the FHT was significantly more reliable than the other three edge-detection techniques in identifying cracks.
Citations
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Journal ArticleDOI
TL;DR: This article proposes a vision‐based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features, and shows quite better performances and can indeed find concrete cracks in realistic situations.
Abstract: A number of image processing techniques IPTs have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real-world situations e.g., lighting and shadow changes can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision-based method using a deep architecture of convolutional neural networks CNNs for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions e.g., strong light spot, shadows, and very thin cracks. Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations.

1,898 citations

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 or methods from "Analysis of edge-detection techniqu..."

  • ...…with realworld situations using pre- and postprocessing methods to detect one specific type of structural damage, such as concrete cracks (Abdel-Qader et al., 2003; Nishikawa et al., 2012), concrete spalling (German et al., 2012), steel cracks (Yeum and Dyke, 2015), loosened bolts (Park…...

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  • ...Most studies have focused on image-processing techniques (IPTs) and testing their compatibility with realworld situations using pre- and postprocessing methods to detect one specific type of structural damage, such as concrete cracks (Abdel-Qader et al., 2003; Nishikawa et al., 2012), concrete spalling (German et al....

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Journal ArticleDOI
TL;DR: 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.

652 citations


Additional excerpts

  • ...Low-level processing Intermediate-level processing High-level processing...

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Journal ArticleDOI
TL;DR: A convolutional neural network is proposed to detect crack patches in each video frame, while the proposed data fusion scheme maintains the spatiotemporal coherence of cracks in videos, and the Naïve Bayes decision making discards false positives effectively.
Abstract: Regular inspection of nuclear power plant components is important to guarantee safe operations. However, current practice is time consuming, tedious, and subjective, which involves human technicians reviewing the inspection videos and identifying cracks on reactors. A few vision-based crack detection approaches have been developed for metallic surfaces, and they typically perform poorly when used for analyzing nuclear inspection videos. Detecting these cracks is a challenging task since they are tiny, and noisy patterns exist on the components’ surfaces. This study proposes a deep learning framework, based on a convolutional neural network (CNN) and a Naive Bayes data fusion scheme, called NB-CNN, to analyze individual video frames for crack detection while a novel data fusion scheme is proposed to aggregate the information extracted from each video frame to enhance the overall performance and robustness of the system. To this end, a CNN is proposed to detect crack patches in each video frame, while the proposed data fusion scheme maintains the spatiotemporal coherence of cracks in videos, and the Naive Bayes decision making discards false positives effectively. The proposed framework achieves a 98.3% hit rate against 0.1 false positives per frame that is significantly higher than state-of-the-art approaches as presented in this paper.

649 citations


Cites background from "Analysis of edge-detection techniqu..."

  • ...They perform well for concrete or pavement surfaces while the cracks have stronger edges than noisy patterns [5]–[7]....

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Journal ArticleDOI
TL;DR: An overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment and some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented.

500 citations

References
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Journal ArticleDOI
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.

28,073 citations

Journal ArticleDOI
TL;DR: Good generalized these methods and gave elegant algorithms for which one class of applications is the calculation of Fourier series, applicable to certain problems in which one must multiply an N-vector by an N X N matrix which can be factored into m sparse matrices.
Abstract: An efficient method for the calculation of the interactions of a 2' factorial ex- periment was introduced by Yates and is widely known by his name. The generaliza- tion to 3' was given by Box et al. (1). Good (2) generalized these methods and gave elegant algorithms for which one class of applications is the calculation of Fourier series. In their full generality, Good's methods are applicable to certain problems in which one must multiply an N-vector by an N X N matrix which can be factored into m sparse matrices, where m is proportional to log N. This results inma procedure requiring a number of operations proportional to N log N rather than N2. These methods are applied here to the calculation of complex Fourier series. They are useful in situations where the number of data points is, or can be chosen to be, a highly composite number. The algorithm is here derived and presented in a rather different form. Attention is given to the choice of N. It is also shown how special advantage can be obtained in the use of a binary computer with N = 2' and how the entire calculation can be performed within the array of N data storage locations used for the given Fourier coefficients. Consider the problem of calculating the complex Fourier series N-1 (1) X(j) = EA(k)-Wjk, j = 0 1, * ,N- 1, k=0

11,795 citations


"Analysis of edge-detection techniqu..." refers background in this paper

  • ...The Fourier transform~FT! is a frequency-based, discrete trans form that is optimized for machine calculation~Cooley and Tukey 1965!....

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Book
25 Nov 1996
TL;DR: Algorithms for Image Processing and Computer Vision, 2nd Edition provides the tools to speed development of image processing applications.
Abstract: A cookbook of algorithms for common image processing applicationsThanks to advances in computer hardware and software, algorithms have been developed that support sophisticated image processing without requiring an extensive background in mathematics This bestselling book has been fully updated with the newest of these, including 2D vision methods in content-based searches and the use of graphics cards as image processing computational aids Its an ideal reference for software engineers and developers, advanced programmers, graphics programmers, scientists, and other specialists who require highly specialized image processingAlgorithms now exist for a wide variety of sophisticated image processing applications required by software engineers and developers, advanced programmers, graphics programmers, scientists, and related specialistsThis bestselling book has been completely updated to include the latest algorithms, including 2D vision methods in content-based searches, details on modern classifier methods, and graphics cards used as image processing computational aidsSaves hours of mathematical calculating by using distributed processing and GPU programming, and gives non-mathematicians the shortcuts needed to program relatively sophisticated applicationsAlgorithms for Image Processing and Computer Vision, 2nd Edition provides the tools to speed development of image processing applications

1,517 citations

Book
01 Jan 1994
TL;DR: In this article, the authors focus on the process of pavement management, from data acquisition and evaluation to network level priority programming to project level design, construction and maintenance, and on the principles, methods and technology which enable the process to become a working system.
Abstract: This book focuses on the process of pavement management, from data acquisition and evaluation to network level priority programming to project level design, construction and maintenance, and on the principles, methods and technology which enable the process to become a working system Examples of working systems are provided, as well as guidelines for implementation Research management, future issues, special problems, new technologies and innovation opportunities are also addressed

508 citations

Journal ArticleDOI
A. Bruce1, D. Donoho, H.-Y. Gao
TL;DR: How localized waveforms are powerful building blocks for signal analysis and rapid prototyping-and how they are now available in software toolkits is described.
Abstract: As every engineering student knows, any signal can be portrayed as an overlay of sinusoidal waveforms of assorted frequencies. But while classical analysis copes superbly with naturally occurring sinusoidal behavior-the kind seen in speech signals-it is ill-suited to representing signals with discontinuities, such as the edges of features in images. Latterly, another powerful concept has swept applied mathematics and engineering research: wavelet analysis. In contrast to a Fourier sinusoid, which oscillates forever, a wavelet is localized in time-it lasts for only a few cycles. Like Fourier analysis, however, wavelet analysis uses an algorithm to decompose a signal into simpler elements. Here, the authors describe how localized waveforms are powerful building blocks for signal analysis and rapid prototyping-and how they are now available in software toolkits.

293 citations