Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Similarity Metric Learning for 2D to 3D Registration of Brain Vasculature Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Automated Rebar Detection for Ground-Penetrating Radar

verfasst von : Spencer Gibb, Hung Manh La

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Automated rebar detection in images from ground-penetrating radar (GPR) is a challenging problem and difficult to perform in real-time as a result of relatively low contrast images and the size of the images. This paper presents a rebar localization algorithm, which can accurately locate the pixel locations of rebar within a GPR scan image. The proposed algorithm uses image classification and statistical methods to locate hyperbola signatures within the image. The proposed approach takes advantage of adaptive histogram equalization to increase the visual signature of rebar within the image despite low contrast. A Naive Bayes classifier is used to approximately locate rebar within the image with histogram of oriented gradients feature vectors. In addition, a histogram based method is applied to more precisely locate individual rebar in the image, and then the proposed methods are validated using existing GPR data and data collected during the course of the research for this paper.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel A Comparative Analysis of Deep and Shallow Features for Multimodal Face Recognition in a Novel RGB-D-IR Dataset
Nächstes Kapitel Improving Visual Feature Representations by Biasing Restricted Boltzmann Machines with Gaussian Filters
Literatur
1.
Simi, A., Manacorda, G., Benedetto, A.: Bridge deck survey with high resolution ground penetrating radar. In: 2012 14th International Conference on Ground Penetrating Radar (GPR), pp. 489–495 (2012)
2.
Krause, V., Abdel-Qader, I., Abudayyeh, O.: Detection and classification of small perturbations in GPR scans of reinforced concrete bridge decks. In: 2012 IEEE International Conference on Electro/Information Technology (EIT), pp. 1–4 (2012)
3.
Hai-zhong, Y., Yu-feng, O., Hong, C.: Application of ground penetrating radar to inspect the metro tunnel. In: 2012 14th International Conference on Ground Penetrating Radar (GPR), pp. 759–763 (2012)
4.
Marecos, V., Fontul, S., Antunes, M.L., Solla, M.: Assessment of a concrete pre-stressed runway pavement with ground penetrating radar. In: 2015 8th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), pp. 1–4 (2015)
5.
Shaw, M., Millard, S., Molyneaux, T., Taylor, M., Bungey, J.: Location of steel reinforcement in concrete using ground penetrating radar and neural networks. NDT E Int. 38, 203–212 (2005). Structural Faults and RepairCrossRef
6.
Kaur, P., Dana, K.J., Romero, F.A., Gucunski, N.: Automated GPR rebar analysis for robotic bridge deck evaluation. IEEE Trans. Cybern. 46, 2265–2276 (2016)CrossRef
7.
Al-Nuaimy, W., Huang, Y., Nakhkash, M., Fang, M., Nguyen, V., Eriksen, A.: Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition. J. Appl. Geophys. 43, 157–165 (2000)CrossRef
8.
La, H.M., Lim, R.S., Basily, B.B., Gucunski, N., Yi, J., Maher, A., Romero, F.A., Parvardeh, H.: Mechatronic systems design for an autonomous robotic system for high-efficiency bridge deck inspection and evaluation. IEEE/ASME Trans. Mechatron. 18, 1655–1664 (2013)CrossRef
9.
La, H.M., Gucunski, N., Kee, S.H., Yi, J., Senlet, T., Nguyen, L.: Autonomous robotic system for bridge deck data collection and analysis. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1950–1955 (2014)
10.
Xianqi-He, Z.-Z., Guangyin-Lu, Q.-L.: Bridge management with GPR. In: 2009 International Conference on Information Management, Innovation Management and Industrial Engineering, vol. 3, pp. 325–328 (2009)
11.
Pasolli, E., Melgani, F., Donelli, M.: Automatic analysis of GPR images: a pattern-recognition approach. IEEE Trans. Geosci. Remote Sens. 47, 2206–2217 (2009)CrossRef
12.
Zhao, Y., Chen, J., Ge, S.: Maxwell curl equation datuming for GPR test of tunnel grouting based on kirchhoff integral solution. In: 2011 6th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), pp. 1–6 (2011)
13.
Shi, H., Liu, Y.: Naïve Bayes vs. support vector machine: resilience to missing data. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds.) AICI 2011. LNCS (LNAI), vol. 7003, pp. 680–687. Springer, Heidelberg (2011). doi:10.​1007/​978-3-642-23887-1_​86 CrossRef
14.
Wang, Z.W., Zhou, M., Slabaugh, G.G., Zhai, J., Fang, T.: Automatic detection of bridge deck condition from ground penetrating radar images. IEEE Trans. Autom. Sci. Eng. 8, 633–640 (2011)CrossRef
15.
Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Romeny, B.T.H., Zimmerman, J.B.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39, 355–368 (1987)CrossRef
16.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893 (2005)
17.
Pang, Y., Zhang, K., Yuan, Y., Wang, K.: Distributed object detection with linear SVMs. IEEE Trans. Cybern. 44, 2122–2133 (2014)CrossRef
18.
Nigam, S., Khare, M., Srivastava, R.K., Khare, A.: An effective local feature descriptor for object detection in real scenes. In: 2013 IEEE Conference on Information Communication Technologies (ICT), pp. 244–248 (2013)
19.
Lim, R.S., La, H.M., Shan, Z., Sheng, W.: Developing a crack inspection robot for bridge maintenance. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 6288–6293 (2011)
20.
Lim, R.S., La, H.M., Sheng, W.: A robotic crack inspection and mapping system for bridge deck maintenance. IEEE Trans. Autom. Sci. Eng. 11, 367–378 (2014)CrossRef
21.
La, H.M., Gucunski, N., Kee, S.H., Nguyen, L.V.: Data analysis and visualization for the bridge deck inspection and evaluation robotic system. Visual. Eng. 3, 6 (2015)CrossRef
22.
La, H.M., Gucunski, N., Lee, S.H., Nguyen, L.V.: Visual and acoustic data analysis for the bridge deck inspection robotic system. In: The 31st International Symposium on Automation and Robotics in Construction and Mining (ISARC), pp. 50–57 (2014)
Metadaten
Titel
Automated Rebar Detection for Ground-Penetrating Radar
verfasst von
Spencer Gibb
Hung Manh La
Copyright-Jahr
2016
Buch
Advances in Visual Computing

Print ISBN: 978-3-319-50834-4

Electronic ISBN: 978-3-319-50835-1

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-50835-1_73