Critical Assessment of Pavement Distress Segmentation Methods

Tsai, Yichang; Kaul, Vivek; Mersereau, R.M.

doi:10.1061/(asce)te.1943-5436.0000051

Cited by 191 publications

(74 citation statements)

References 31 publications

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“…Many of the methods for pavement distress detection are based on the assumption that distress pixels are darker than the background. Wang [90] and Tsai et al [91] have concluded that such methods perform differently well according to varying lighting conditions and shadows. Figure 2 illustrates the so-called checker shadow illusion [92].…”

Section: Asphalt Pavements 341 Pre-processingmentioning

confidence: 99%

“…Based on statistical measures of the pixel intensities, thresholding methods that classify pixels as crack or non-crack pixels are applied. Tsai et al [91] have made a critical assessment of distress segmentation methods, in particular statistical thresholding, Canny edge detection, multiscale wavelets, crack seed verification, iterative clipping methods, and dynamic optimization based methods. Koutsopoulos et al [109] developed an algorithm for crack image segmentation based on a model that describes the statistical properties of pavement images.…”

Section: Cracksmentioning

confidence: 99%

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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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Section: Asphalt Pavements 341 Pre-processingmentioning

confidence: 99%

Section: Cracksmentioning

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

View full text Add to dashboard Cite

show abstract

“…A comprehensive analysis of multi-resolution was proposed, where the determination of texture characteristic curves was based on the Haar, Daubechies, Coiflet, ridgelet and curvelet transformations. A critical assessment of multi-resolution analysis and statistical thresholding methods, as well as edge detection and wavelet transforms, is provided in [14].…”

Section: Introductionmentioning

confidence: 99%

Detection of Cracks in Asphalt Pavement During Road Inspection Processes

Staniek

2017

SJSUT.ST

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DETECTION OF CRACKS IN ASPHALT PAVEMENT DURING ROAD INSPECTION PROCESSESSummary. Road inspection is one of key processes of a pavement management system, whose function is to examine and describe the road infrastructure condition. When thoroughly performed, it provides the information required to implement an adequate road infrastructure maintenance policy and plan ad hoc repairs or refurbishments. This article discusses a solution for automatic asphalt pavement cracking detection, based on image-processing technology. This solution makes it possible to identify different crack types, i.e., transverse, longitudinal, alligator-type and technological cracks. The detection process is based on the application of various methods, including statistical difference identification for pre-assumed image analysis directions, i.e., in and opposite to the test vehicle running direction. The purpose of the morphological and filtering operations applied was to reduce the image noise level. The solution proposed was verified using video material in the form of a sequence of images recorded using the test vehicle.

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“…Six different algorithms were used and evaluated with images taken near the city of Atlanta, USA, with varying lighting conditions, shadows and cracks [5]. Lin and Liu used Support Vector Machine (SVM), a topology of artificial intelligence similar to neural networks, for assessment of potholes on pavement pictures.…”

Section: Introductionmentioning

confidence: 99%

Pavement Distress Detection With Picucha Methodology for Area-Scan Cameras and Dark Images

Salini¹,

Xu²,

Paplauskas³

2017

CEJ

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The PICture Unsupervised Classification with Human Analysis (PICUCHA) refers to a hybrid human-artificial intelligence methodology for pavement distresses assessment. It combines the human flexibility to recognize patterns and features in images with the neural network ability to expand such recognition to large volumes of images. In this study, the PICUCHA performance was tested with images taken with area-scan cameras and flash light illumination over a pavement with dark textures. These images are particularly challenging for the analysis because of the lens distortion and non-homogeneous illumination, generating artificial joints that happened at random positions inside the image cells. The chosen images were previously analyzed by other software without success because of the dark coluor. The PICUCHA algorithms could analyze the images with no noticeable problem and without any image pre-processing, such as contrast or brightness adjustments. Because of the special procedure used by the pavement engineer for the key patterns description, the distresses detection accuracy of the PICUCHA for the particular image set could reach 100%.

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Critical Assessment of Pavement Distress Segmentation Methods

Cited by 191 publications

References 31 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

Detection of Cracks in Asphalt Pavement During Road Inspection Processes

Pavement Distress Detection With Picucha Methodology for Area-Scan Cameras and Dark Images

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