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Proceedings ArticleDOI: 10.1109/IADCC.2009.4809101

Automated Assessment Tool for the Depth of Pipe Deterioration

06 Mar 2009-pp 721-724
Abstract: Defects in underground pipeline images are indicative of the condition of buried infrastructures like sewers and water mains. This paper entitled Automated Assessment Tool for the depth of pipe deterioration presents a three step method which is a simple, robust and efficient one to detect defects in the underground concrete pipes. It identifies and extracts defect-like structures from pipe images whose contrast has been enhanced. We propose to use segmentation and feature extraction using structural elements. The main objective behind using this tool is to find the dimensions of the defect such as the length, width and depth and also the type of defect. The detection of defects in buried pipes is a crucial step in assessing the degree of pipe deterioration for municipal operators. Although the human eye is extremely effective at recognition and classification, it is not suitable for assessing pipe defects in thousands of miles of pipeline because of fatigue, subjectivity and cost. Our objective is to reduce the effort and the labour of a person in detecting the defects in underground pipes.

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Topics: Pipeline transport (51%)
Citations
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Journal ArticleDOI: 10.1016/J.AUTCON.2019.103061
Abstract: This survey presents an in-depth overview of the last 25 years of research within the field of image-based automation of Closed-Circuit Television (CCTV) and Sewer Scanner and Evaluation Technology (SSET) sewer inspection. The survey investigates both the algorithmic pipeline, and the datasets and corresponding evaluation protocols. As a result of the in-depth survey, several trends within the research field are revealed, discussed, and future research directions are proposed. Based on the conducted survey, we put forth a set of three recommendations, which we believe will further improve and open the research field, as well as make the future research more reproducible: 1) The introduction of free and public benchmark datasets, 2) Standardized evaluation metrics, and 3) Open-sourcing the associated code.

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26 Citations


Journal ArticleDOI: 10.1108/K-12-2012-0126
28 Jul 2014-Kybernetes
Abstract: Purpose – The purpose of this paper is to provide a way to analyze satellite images using various clustering algorithms and refined bitplane methods with other supporting techniques to prove the superiority of RIFCM. Design/methodology/approach – A comparative study has been carried out using RIFCM with other related algorithms from their suitability in analysis of satellite images with other supporting techniques which segments the images for further process for the benefit of societal problems. Four images were selected dealing with hills, freshwater, freshwatervally and drought satellite images. Findings – The superiority of the proposed algorithm, RIFCM with refined bitplane towards other clustering techniques with other supporting methods clustering, has been found and as such the comparison, has been made by applying four metrics (Otsu (Max-Min), PSNR and RMSE (40%-60%-Min-Max), histogram analysis (Max-Max), DB index and D index (Max-Min)) and proved that the RIFCM algorithm with refined bitplane yi...

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Topics: Cluster analysis (55%)

16 Citations


Open access
01 Jan 2014-
Abstract: The Images obtained through remote sensing systems are not often sufficient for high precision applications due to various distortions. The distortions can be due to errors like geometric errors, etc. Also multi-date satellite images of the same area under different conditions are difficult to compare because of change in atmospheric propagation, sensor response and illuminations. Keeping these points in view, in this paper, we deal with the first phase of pre-processing and we make the satellite images free from such errors and use clustering techniques With Geometric Correction (WGC) and Without Geometric Correction (WOGC) applied to the satellite images using our proposed algorithm. Finally, the image is reconstructed with depth dimension/depth map generation for the anaglyph image for better interpretation of satellite imagery. We have made experimental analysis of our algorithm using suitable satellite images and found the results to be very encouraging.

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Topics: Depth map (55%), Anaglyph 3D (55%), Satellite imagery (52%)

7 Citations


Proceedings ArticleDOI: 10.1109/ICICISYS.2009.5357617
Wu Xue-Fei1, Bai Hua1Institutions (1)
28 Dec 2009-
Abstract: Many underground water pipelines are old and approaching their service lives in a great number of cities. With the promotion of sustaining buried infrastructure, it's necessary to pay much attention on how to effectively extract defect characteristics of damaged pipelines. Detection of defects in underground pipes is a crucial step to assess the deterioration degree of pipeline for municipal operators. Based on the image processing theory, a defect feature extracting method under HSV color space is proposed in this paper. QFCM (Quick Fuzzy C-Mean clustering) segmentation arithmetic is applied to extract characteristics parameters. The proposed algorithm can identify defects from background, and the types of defects in the buried pipes can be categorized in the estimation stage. Then, different methodologies of parameters extraction are applied in different types of pipe defects, features like area, angle, length and width of defects can also be calculated. And then, a method of assessing the accuracy of feature extraction algorithm is discussed. Finally, the proposed detection approach has been experimentally tested using a group of images acquired by CCD camera from real inspection scenarios. The experimental results proved that it is feasible and effective to apply the system in feature extraction of pipe defects of the underground water-pipelines.

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Topics: Feature extraction (55%), Feature (computer vision) (54%), Image processing (52%) ...read more

4 Citations


Open accessJournal ArticleDOI: 10.1515/CAIT-2015-0011
Abstract: Several image segmentation techniques have been developed over the years to analyze the characteristics of images. Among these, the uncertainty based approaches and their hybrids have been found to be more efficient than the conventional and individual ones. Very recently, a hybrid clustering algorithm, called Rough Intuitionistic Fuzzy C-Means RIFCM was proposed by the authors and proved to be more efficient than the conventional and other algorithms applied in this direction, using various datasets. Besides, in order to remove noise from the images, a Refined Bit Plane RBP algorithm was introduced by us. In this paper we use a combination of the RBP and RIFCM to propose an approach and apply it to leukemia images. The aim of the paper is twofold. First, it establishes the superiority of our approach in medical diagnosis in comparison to most of the conventional, as well as uncertainty based approaches. The other objective is to provide a computer-aided diagnosis system that will assist the doctors in evaluating medical images in general, and also in easy and better assessment of the disease in leukaemia patients. We have applied several measures like DB-index, D-index, RMSE, PSNR, time estimation in depth computation and histogram analysis to support our conclusions.

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Topics: Cluster analysis (55%), Image segmentation (54%), Bit plane (52%) ...read more

3 Citations


References
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Journal ArticleDOI: 10.1016/J.AUTCON.2005.02.006
Sunil K. Sinha1, Paul Fieguth2Institutions (2)
Abstract: The detection of cracks in concrete infrastructure is a problem of great interest. In particular, the detection of cracks in buried pipes is a crucial step in assessing the degree of pipe deterioration for municipal and utility operators. The key challenge is that whereas joints and laterals have a predictable appearance, the randomness and irregularity of cracks make them difficult to model. Our previous work has led to a segmented pipe image (with holes, joints, and laterals eliminated) obtained by a morphological approach. This paper presents the development of a statistical filter for the detection of cracks in the pipes. We propose a two-step approach. The first step is local and is used to extract crack features from the buried pipe images; we present two such detectors as well as a method for fusing them. The second step is global and defines the cracks among the segment candidates by processes of cleaning and linking. The influences of the parameters on crack detection are studied and results are presented for various pipe images.

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224 Citations


Journal ArticleDOI: 10.1016/J.AUTCON.2005.02.007
Sunil K. Sinha1, Paul Fieguth2Institutions (2)
Abstract: The enormity of the problem of deteriorating pipeline infrastructure is widely apparent. Since a complete rebuilding of the piping system is not financially realistic, municipal and utility operators require the ability to monitor the condition of buried pipes. Thus, reliable pipeline assessment and management tools are necessary to develop long term cost effective maintenance, repair, and rehabilitation programs. In this paper a simple, robust and efficient image segmentation algorithm for the automated analysis of scanned underground pipe images is presented. The algorithm consists of image pre-processing followed by a sequence of morphological operations to accurately segment pipe cracks, holes, joints, laterals, and collapsed surfaces, a crucial step in the classification of defects in underground pipes. The proposed approach can be completely automated and has been tested on five hundred scanned images of buried concrete sewer pipes from major cities in North America.

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97 Citations


Journal ArticleDOI: 10.1016/S0926-5805(99)00007-2
Osama Moselhi1, Tariq Shehab-Eldeen1Institutions (1)
Abstract: Automation is gaining momentum in industry, particularly in rehabilitation and inspection works of underground infrastructure facilities. This paper describes a model for automating inspection and identification of surface defects in underground water and sewer pipes. The paper describes the current efforts in identification of surface defects in underground water and sewer mains, and presents an automated system designed to assist infrastructure engineers in diagnosing defects in this class of pipe networks. It describes the general architecture of the system and its basic components, and focuses primarily on four modules designed for automating image acquisition, image processing, features extraction and classification of defects.

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84 Citations


Journal ArticleDOI: 10.1007/S00138-005-0012-0
K. Sinha1, W. Fieguth2Institutions (2)
27 Mar 2006-
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

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Topics: Image segmentation (53%)

32 Citations

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