Proceedings ArticleDOI
Automated analysis and detection of cracks in underground scanned pipes
Paul Fieguth,Sunil K. Sinha +1 more
- Vol. 4, pp 395-399
TLDR
This paper presents ongoing research into the automatic assessment of the structural condition of underground pipes for the purpose of preventive maintenance by municipalities.Abstract:
Closed circuit television (CCTV) surveys are used widely in North America to assess the structural integrity of underground pipes. The video images are examined visually and classified into grades according to degrees of damage. The human eye is extremely effective at recognition and classification, but it is not suitable for assessing pipe defects in thousands of miles of pipeline images due to fatigue, subjectivity and cost. This paper presents ongoing research into the automatic assessment of the structural condition of underground pipes for the purpose of preventive maintenance by municipalities.read more
Citations
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Journal ArticleDOI
Underground asset location and condition assessment technologies
TL;DR: There is no single technology which can locate all underground utility services with complete certainty or that can provide a means of condition assessment that is generally applicable, and the development of multi-sensor tools is advocated both for the location and condition assessment of pipelines.
Journal ArticleDOI
A robust approach for automatic detection and segmentation of cracks in underground pipeline images
Shivprakash Iyer,Sunil K. Sinha +1 more
TL;DR: The proposed method is based on mathematical morphology and curvature evaluation that detects crack-like patterns in a noisy environment and statistically evaluates its accuracy and robustness with respect to varying pipe background color, crack geometries and background noise.
Journal ArticleDOI
Segmentation of buried concrete pipe images
Sunil K. Sinha,Paul Fieguth +1 more
TL;DR: In this paper, a simple, robust and efficient image segmentation algorithm for the automated analysis of scanned underground pipe images is presented, consisting 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.
Journal ArticleDOI
Segmentation of Pipe Images for Crack Detection in Buried Sewers
Shivprakash Iyer,Sunil K. Sinha +1 more
TL;DR: This article presents the development of an automated data analysis system for detecting defects in sanitary sewer pipelines and proposes a three-step method to identify and extract cracks from contrast enhanced pipe images based on mathematical morphology and curvature evaluation.
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.
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A robust approach for automatic detection and segmentation of cracks in underground pipeline images
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