Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine

Halfawy, Mahmoud R.; Hengmeechai, Jantira

doi:10.1016/j.autcon.2013.10.012

Cited by 129 publications

(53 citation statements)

References 30 publications

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“…Methods include edge detection [25] [22] or the Hough transform [22] for edge/line detection, image segmentation [26] and background subtraction [18] for foreground object extraction, methods of image registration [18] and optical flow [24] for the tracking and association of objects in successive video frames, particularly relevant in CCTV imaging. More advanced methods include texture-based methods, including co-occurrence [21] and histograms of oriented gradients [23], and multi-resolution or wavelet-based approaches [29] [17]. Not all of these methods can be described here, and the reader is referred to a comprehensive review [81].…”

Section: Feature Extractionmentioning

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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Section: Feature Extractionmentioning

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

“…The exploitation of more sophisticated features has also been proposed. Histogram of Oriented Gradient (HOGs) features and SVMs are utilized in the work of Halfawy and Hengmeechai (2014).. Shape-based filtering is another approach, exploited in the work of Jahanshahi et al (2013) for crack detection and quantification.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Robotic Inspection in Tunnels

Protopapadakis

Stentoumis

Doulamis

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, an automatic robotic inspector for tunnel assessment is presented. The proposed platform is able to autonomously navigate within the civil infrastructures, grab stereo images and process/analyse them, in order to identify defect types. At first, there is the crack detection via deep learning approaches. Then, a detailed 3D model of the cracked area is created, utilizing photogrammetric methods. Finally, a laser profiling of the tunnel's lining, for a narrow region close to detected crack is performed; allowing for the deduction of potential deformations. The robotic platform consists of an autonomous mobile vehicle; a crane arm, guided by the computer vision-based crack detector, carrying ultrasound sensors, the stereo cameras and the laser scanner. Visual inspection is based on convolutional neural networks, which support the creation of high-level discriminative features for complex non-linear pattern classification. Then, real-time 3D information is accurately calculated and the crack position and orientation is passed to the robotic platform. The entire system has been evaluated in railway and road tunnels, i.e. in Egnatia Highway and London underground infrastructure.

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“…Towards this direction, complex handcrafted features are constructed, which in turn are used to train a learning model (detection methods). Some commonly used handcrafted features for VI are: edges (Abdel-Qader et al, 2003), colour intensity (Son et al, 2012), texture descriptors (Koch and Brilakis, 2011), entropy (German et al, 2012), and HOG (Halfawy and Hengmeechai, 2014); while common learning models are fuzzy/neuro-fuzzy inference (Kawamura and Miyamoto, 2003;Zhao and Chen, 2002), SVMs (Nashat et al, 2014) and kNN classifiers (Jahanshahi et al, 2013). More information about VI in large concrete structures can be found in (Koch et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The exploitation of more sophisticated features has also been proposed. HOG features and SVMs are utilized in the work of (Halfawy and Hengmeechai, 2014), to support automated detection and classification of pipe defects. Shape-based filtering is exploited in the work of (Jahanshahi et al, 2013) for crack detection and quantification.…”

Section: Introductionmentioning

confidence: 99%

A Holistic Approach for Inspection of Civil Infrastructures Based on Computer Vision Techniques

Stentoumis

Protopapadakis

Doulamis

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this work, it is examined the 2D recognition and 3D modelling of concrete tunnel cracks, through visual cues. At the time being, the structural integrity inspection of large-scale infrastructures is mainly performed through visual observations by human inspectors, who identify structural defects, rate them and, then, categorize their severity. The described approach targets at minimum human intervention, for autonomous inspection of civil infrastructures. The shortfalls of existing approaches in crack assessment are being addressed by proposing a novel detection scheme. Although efforts have been made in the field, synergies among proposed techniques are still missing. The holistic approach of this paper exploits the state of the art techniques of pattern recognition and stereo-matching, in order to build accurate 3D crack models. The innovation lies in the hybrid approach for the CNN detector initialization, and the use of the modified census transformation for stereo matching along with a binary fusion of two state-of-the-art optimization schemes. The described approach manages to deal with images of harsh radiometry, along with severe radiometric differences in the stereo pair. The effectiveness of this workflow is evaluated on a real dataset gathered in highway and railway tunnels. What is promising is that the computer vision workflow described in this work can be transferred, with adaptations of course, to other infrastructure such as pipelines, bridges and large industrial facilities that are in the need of continuous state assessment during their operational life cycle.

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Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine

Cited by 129 publications

References 30 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

Autonomous Robotic Inspection in Tunnels

A Holistic Approach for Inspection of Civil Infrastructures Based on Computer Vision Techniques

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