Wavelet-based pavement distress detection and evaluation

Zhou, Jian; Huang, Peisen S.; Chiang, Fu‐Pen

doi:10.1117/1.2172917

Cited by 182 publications

(68 citation statements)

References 14 publications

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“…Zhou et al [101] use wavelet transform to decompose an image into approximation and detail coefficients. The detail coefficients represent distress in the pavement images.…”

Section: Defect Detectionmentioning

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

761

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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“…Zhou et al [101] use wavelet transform to decompose an image into approximation and detail coefficients. The detail coefficients represent distress in the pavement images.…”

Section: Defect Detectionmentioning

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

761

336

View full text Add to dashboard Cite

show abstract

“…In last decade several authors have proposed a method of roughness data processing use of the different wavelet transformation and analysis techniques to obtain detailed roughness features of interest and to extract useful information for pavement maintenance management (Wei et al 2005, Zhou et al 2006, Ayenu-Prah and Attoh-Okine 2009, Tomiyama et al 2013. Cantisani and Loprencipe (2010) proposed the new speed-related IRI thresholds.…”

Section: Other New Approachesmentioning

confidence: 99%

Current approaches to quantify the longitudinal road roughness

Múčka

2015

International Journal of Pavement Engineering

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Paper summarised the proposals of fourteen new alternative indexes intended for road roughness evaluation of longitudinal road profile, which have been introduced in the last decade approximately. The main advantages of proposed indexes are: (1) effort to reflect the most of ride quality aspects such as ride comfort, ride safety and dynamic load of road or cargo; (2) using the in situ measurements of vibration response or subjective perception of vibration; (3) effort to reflect the vehicle responses such as roll and pitch vibration, or longitudinal and lateral vibration; (4) effort to detect the critical parts of road in a spatial domain; and (5) effort to reflect a vehicle velocity. The main limitations of proposed indexes are: (1) many proposals are only alternative to the international roughness index (IRI) or are based on the IRI procedure; (2) a simple one-dimensional vehicle model is often used with the constant parameters and velocity; and (3) orientation to one vibration response category as ride comfort.

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“…Detection provides the additional information of where an object is located within the given data. Hence, some methods are restricted to only recognizing the presence of a defect and classifying images between intact and healthy pavement without specifying the defect type nor which part of the image they occupy [50]. Others that use image thresholding techniques are not capable of distinguishing patches from potholes [51][52][53].…”

Section: Introductionmentioning

confidence: 99%

Patch detection for pavement assessment

Radopoulou

Brilakis

2015

Automation in Construction

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Pavement management systems rely on comprehensive up-to-date road condition data to provide effective decision support for short, medium and long term maintenance scheduling. However, the cost per mile of the existing condition data collection methods allows only for periodical surveys. This leads to long gaps between inspections and a focus on major roads over rural ones. Therefore, pavement condition monitoring systems that provide inexpensive frequent updates on the road condition are necessary. Such systems would require robust and automatic defect detection methods using low-cost sensors. In this paper, one such method is proposed for detecting road patches from video data acquired by the car's parking camera. A patch is initially detected based on its visual characteristics, which are: 1) it consists of a closed contour and 2) its texture is the same with the surrounding intact pavement. The patch is then passed to a kernel tracker in order to trace it in subsequent video frames. This way redetection is avoided and each patch is reported only once. The method was implemented in a C# prototype and tested with video data consisting of approximately 4000 frames collected from roads in Cambridge, UK. The results show that the suggested method has 84% precision and 96% recall.

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Wavelet-based pavement distress detection and evaluation

Cited by 182 publications

References 14 publications

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

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

Current approaches to quantify the longitudinal road roughness

Patch detection for pavement assessment

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