Computer-vision based crack detection and analysis

Prasanna, Prateek; Dana, Kristin; Gucunski, Nenad; Basily, Basily

doi:10.1117/12.915384

Cited by 53 publications

(32 citation statements)

References 5 publications

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“…In this case, the accuracy of results varied with camera pose and distance from where images are taken. Prasanna et al [52] developed a histogram-based classification algorithm and used it along with Support Vector Machines to detect cracks on a concrete deck surface. The results of this algorithm on real bridge data highlighted the need for improving the accuracy.…”

Section: Crackingmentioning

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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Section: Crackingmentioning

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

“…Methods based on image analysis have also been exploited in the literature, ranging from detection of welding defects in pipelines [17] to concrete surface analysis [18] and the protection of cultural heritage [19]. Thermographic image analysis systems have recently been proposed for performing in situ nondestructive inspections during thermomechanical fatigue tests [20]; the system showed a high sensitivity, being able to detect cracks smaller than 500 µm.…”

Section: State Of the Artmentioning

confidence: 99%

A Knowledge-Based Approach to Crack Detection in Thermographic Images

Ghidoni

Antonello

Nanni

et al. 2015

Intelligent Autonomous Systems 13

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This paper describes an innovative visual inspection system for the detection of small cracks in metal parts. Given the extremely low dimension of the defects to be detected, the system is based on a thermographic approach: defects are recognized analyzing the heat flux induced by an excitation. The system is able to analyze parts of very high complexity, like a crankshaft, thanks to the introduction of an articulated robot, used for moving the part. The system also benefits from a deep knowledge of the inspected part and of the imaging system: this is exploited to reduce the high number of artifacts and reflections that appear in thermographic images when heat sources are employed. The core of the inspection mechanism is a computer vision algorithm that is capable of analyzing the thermographic images, extract the thermal information, and exploit a Support Vector Machine (SVM) classifier to provide a final decision on the presence of a crack in the analyzed part.

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“…The image is partitioned into cells having quasi-uniform illumination. In [9], the threshold of selected cells is function of the average intensity value. In [10], a probabilistic relaxation for coarse crack detection and noise elimination is performed to produce a binary image without any parameter to optimize.…”

Section: Related Workmentioning

confidence: 99%

Automatic crack detection on concrete floor images

Simler

Trostmann

Berndt

2019

Photonics and Education in Measurement Science 2019

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This paper presents an algorithm detecting automatically a wide variety of cracks on monochrome images of concrete floors. It is part of a vision system, generating a crack map to support the condition monitoring for buildings. The suggested method uses successively radiometric, geometric and contextual information. An automatic supervised adaptive intensity-threshold method handles radiometry information. A threshold-based method was chosen because it can separate even thin cracks from the background. In order to cope with different image cluster intensities, first a clustering algorithm separates regions of different intensities, and second the threshold is adaptive at the cluster level; being a continuous function of the cluster intensity. At some points, function values were learned via supervised classification. Then, we performed an interpolation between these points in order to get a threshold whatever the cluster intensity (continuity). At this step, we have a thresholded image. However, due to texture, non-crack dark defects and dirtiness we have false positives. In order to overcome this problem, connected pixels are grouped into regions, and after discarding small regions, a size-dependent geometrical shape filter is suggested. The shape of a crack region depends on its area, and this relation was empirically learned. A region is retained if its shape features are above some area depending thresholds. However, mainly due to texture, we have still false positives because some non-crack entities have radiometry and geometry of cracks. Fortunately, they are often small isolated regions and are discarded via an isolation filter. Tests performed on many images show very encouraging results.

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Computer-vision based crack detection and analysis

Cited by 53 publications

References 5 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

A Knowledge-Based Approach to Crack Detection in Thermographic Images

Automatic crack detection on concrete floor images

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