PIRAT—A System for Quantitative Sewer Pipe Assessment

Kirkham, R.; Kearney, P. D.; Rogers, Kevin; Mashford, John

doi:10.1177/02783640022067959

Cited by 90 publications

(44 citation statements)

References 17 publications

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“…PIRAT [89,90] was developed to detect, classify, and rate defects using artificial intelligence by building a cylindricalpolar geometric model of the interior of the sewer using a scanner (laser or sonar) carried by a robotic in-pipe vehicle. The vehicle carries the scanner along the centerline of the sewer and has a forward-facing color video camera, lighting, and other sensors.…”

Section: 11mentioning

confidence: 99%

Water and Wastewater Pipe Nondestructive Evaluation and Health Monitoring: A Review

Rizzo

2010

Advances in Civil Engineering

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pipe networks assure the transportation of primary commodities such as water, oil, and natural gas. The quantitative and early detection of defects in pipes is critical in order to avoid severe consequences. As a result of high-profile accidents and economic downturn, research and development in the area of pipeline inspection has focused mainly on gas and oil pipelines. Due to the low cost of water, the development of nondestructive inspection (NDI) and structural health monitoring (SHM) technologies for fresh water mains and sewers has received the least attention. Moreover, the technical challenges associated with the practical deployment of monitoring system demand synergistic interaction across several disciplines, which may limit the transition from laboratory to real structures. This paper presents an overview of the most used NDI/SHM technologies for freshwater pipes and sewers. The challenges that said infrastructures pose with respect to oil and natural gas pipeline networks will be discussed. Finally, the methodologies that can be translated into SHM approaches are highlighted.

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Section: 11mentioning

confidence: 99%

Water and Wastewater Pipe Nondestructive Evaluation and Health Monitoring: A Review

Rizzo

2010

Advances in Civil Engineering

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“…Although this 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 17 limitation is frustrating, it strongly motivates continued research work on machine intelligence and computer vision in this application, and is the driving motivation for this section. There have been significant with computer vision contributions to pipe inspection, in whole integrated systems such as PIRAT [28] [18], KARO [18], and AIMP [18] [79], and the mapping the underworld (MTU) project [19]. The computer vision analysis of underground concrete sewer pipes has much in common with other forms of infrastructure.…”

Section: Underground Concrete Pipesmentioning

confidence: 99%

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

Koch

Georgieva

Kasireddy

et al. 2015

Advanced Engineering Informatics

797

336

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(2015) A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics, 29 (2). pp. 196-210. ISSN 1474-0346 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/31986/1/Manuscript_KochEtAl_2015_accepted.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the Creative Commons Attribution Non-commercial No Derivatives licence and may be reused according to the conditions of the licence. For more details see: http://creativecommons.org/licenses/by-nc-nd/2.5/ A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 Abstract:To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. 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. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research. Keywords:Computer Vision, Infrastructure, Condition assessment, Defect detection, Infrastructure monitoring Research Highlights: Visual inspection of civil infrastructure is essential for condition assessment.  We focus on concrete bridges, tunnels, underground pipes, and asphalt pavements.  Accordingly, we review the latest computer vision based defect detection methods.  Using computer vision most relevant types of defects can be automatically detected.  Automatic defect properties retrieval and assessment has not been achieved yet . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 ...

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“…However the subjective nature of such assessments has spurred much research into the automated processing of the resulting video data (see for example [1][2][3]). Many of these visual inspection techniques are looking for more than just erosion.…”

Section: Introductionmentioning

confidence: 99%

“…This can be of lower accuracy: one to two millimetres laterally, and 5-20 millimetres along the length of the pipe. (Note that crack detection requires a resolution of better than 0.5mm per pixel [1]. )…”

Section: Introductionmentioning

confidence: 99%