Morphological segmentation and classification of underground pipe images
27 Mar 2006-Vol. 17, Iss: 1, pp 21-31
TL;DR: The experimental results demonstrate that the proposed algorithm can precisely segment and classify pipe cracks, holes, laterals, joints and collapse surface from underground pipe images.
Abstract: Visual inspection based on closed circuit television surveys is used widely in North America to assess the condition of underground pipes. Although the human eye is extremely effective at recognition and classification, it is not suitable for assessing pipe defects in thousand of miles of pipeline because of fatigue, subjectivity, and cost. In this paper, simple, robust, and efficient image segmentation and classification algorithm for the automated analysis of scanned underground pipe images is presented. The experimental results demonstrate that the proposed algorithm can precisely segment and classify pipe cracks, holes, laterals, joints and collapse surface from underground pipe images
Citations
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TL;DR: This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring and presents a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects.
Abstract: Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS) industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification.
231 citations
Cites methods from "Morphological segmentation and clas..."
...In [13], a morphological image processing and thresholding method is applied to detect and classify pipe defects....
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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
Cites background from "Morphological segmentation and clas..."
...Sinha and Fieguth [24] detected defects in underground pipe images by thresholding the morphological opening of the pipe images using different structuring elements....
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TL;DR: An automated approach is developed for detecting sewer pipe defects based on a deep learning technique namely faster region-based convolutional neural network (faster R-CNN) and results demonstrate that dataset size, initialization network type and training mode, and network hyper-parameters have influence on model performance.
209 citations
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
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 "Morphological segmentation and clas..."
...Sinha and Fieguth (2006b) detected the defects in underground pipe images by thresholding the morphological opening of the pipe images using different structuring elements....
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...Neuro-fuzzy systems are promising approaches used by Chae (2001) and Sinha and Fieguth (2006a) to detect defects, including cracks in sewer pipe systems....
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...The performance of the neuro-fuzzy systems proposed in these two researches is better than the regular neural networks and other classical classification algorithms (Chae 2001, Sinha and Fieguth 2006a)....
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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.
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"Morphological segmentation and clas..." refers background in this paper
...A large number of segmentation algorithms have been proposed in the literature [2, 3]....
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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.
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01 Jul 1989-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
TL;DR: The entropy-based thresholding algorithm is extended to the 2-dimensional histogram and it was found that the proposed approach performs better specially when the signal to noise ratio (SNR) is decreased.
Abstract: Automatic thresholding of the gray-level values of an image is very useful in automated analysis of morphological images, and it represents the first step in many applications in image understanding. Recently it was shown that by choosing the threshold as the value that maximizes the entropy of the 1-dimensional histogram of an image, one might be able to separate, effectively, the desired objects from the background. This approach, however, does not take into consideration the spatial correlation between the pixels in an image. Thus, the performance might degrade rapidly as the spatial interaction between pixels becomes more dominant than the gray-level values. In this case, it becomes difficult to isolate the object from the background and human interference might be required. This was observed during studies that involved images of the stomach. The objective of this report is to extend the entropy-based thresholding algorithm to the 2-dimensional histogram. In this approach, the gray-level value of each pixel as well as the average value of its immediate neighborhood is studied. Thus, the threshold is a vector and has two entries: the gray level of the pixel and the average gray level of its neighborhood. The vector that maximizes the 2-dimensional entropy is used as the 2-dimensional threshold. This method was then compared to the conventional 1-dimensional entropy-based method. Several images were synthesized and others were obtained from the hospital files that represent images of the stomach of patients. It was found that the proposed approach performs better specially when the signal to noise ratio (SNR) is decreased. Both, as expected, yielded good results when the SNR was high (more than 12 dB).
688 citations
"Morphological segmentation and clas..." refers background in this paper
...An entropy-based approach [8], which finds a bilevel threshold to maximize entropy criteria, did not improve pavement– surface images....
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