Analysis of Edge-Detection Techniques for Crack Identification in Bridges

Abdel‐Qader, Ikhlas; Abudayyeh, Osama; Kelly, Michael

doi:10.1061/(asce)0887-3801(2003)17:4(255)

Cited by 650 publications

(272 citation statements)

References 6 publications

Supporting

Mentioning

267

Contrasting

Unclassified

Order By: Relevance

“…Building on this categorization, we reviewed and discussed some of the existing algorithms below. Abdel-Qader et al [47] compared various edge detection algorithms and found the Haar Wavelet method to be the most reliable among them, for the purpose of crack detection. However, the performance of edge detection algorithms on noisy image data is questionable, and same is the case with morphological operation based methods [48].…”

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

712

336

View full text Add to dashboard Cite

(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 ...

show abstract

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

712

336

View full text Add to dashboard Cite

show abstract

“…A variety of image processing techniques can be used to characterize the damage in concrete data; among these methods are edge-detection algorithms (Abdel-Qader et al, 2003). Edges are considered to be areas with strong intensity contrasts in an image, causing a jump in intensity from 1 pixel to the next.…”

Section: Advances In Thermography Imaging For Sub-surface Damage Detementioning

confidence: 99%

Thermal Imaging for Enhancing Inspection Reliability: Detection and Characterization

Taib¹,

Jadin²,

Kabir³

2012

Infrared Thermography

View full text Add to dashboard Cite

“…classification via machine learning approaches). Such approaches involve colour properties, different non-RGB colour spaces and various machine learning algorithms , edge detection techniques, Abdel-Qader et al (2003) and graph based search algorithms, Yu et al (2007). .…”

Section: Related Workmentioning

confidence: 99%

Autonomous Robotic Inspection in Tunnels

Protopapadakis¹,

Stentoumis²,

Doulamis³

et al. 2016

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

View full text Add to dashboard Cite

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.

show abstract

Analysis of Edge-Detection Techniques for Crack Identification in Bridges

Cited by 650 publications

References 6 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

Thermal Imaging for Enhancing Inspection Reliability: Detection and Characterization

Autonomous Robotic Inspection in Tunnels

Contact Info

Product

Resources

About