Skip to main content
SpringerLink
Log in

Machine learning-based automatic reinforcing bar image analysis system in the internet of things

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

A Correction to this article was published on 05 July 2018

This article has been updated

Abstract

Research on the analysis of reinforcing bar images has been conducted to count reinforcing bars moving along a conveyor belt at a bar production plant. It is relatively easy to analyze images at the plant, where the environment and light sources can be tightly controlled. At construction sites, the characteristics of images vary greatly depending on the environment, time of image acquisition, and weather conditions. Therefore, a method for correctly segregating the reinforcing bar area is needed. In this paper, we propose an automatic reinforcing bar image analysis system based on machine learning. Our proposed system accurately separates the bar area from the background and counts the number of bars in the image. Compared with existing method, the proposed system performs better on detection of reinforcing bars.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Change history

  • 05 July 2018

    In the original publication, the captured corresponding author was incorrect. The correct corresponding author is Sang Oh Park. The original article has been corrected.

References

  1. Achanta R et al. (2010) SLIC Superpixels. Technical report 149300 EPFL

  2. Bahaa-Eldeen AM et al (2000) Edge detection of binary images using the method of masks. Comput Vision Patt Recogn Ain Shams Univ Facul Eng Sci Bull 35(3):349–355

    Google Scholar 

  3. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  4. Brunelli R (2009) Template matching techniques in computer vision: theory and practice. Wiley, Hoboken, NJ

    Book  Google Scholar 

  5. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge, U.K.

    Book  Google Scholar 

  6. Davies ER (1988) A modified Hough scheme for general circle location. Pattern Recogn Lett 7(1):37–44

    Article  Google Scholar 

  7. Dietterich TG (2002) Ensemble learning. The handbook of brain theory and Neural Network

  8. Ellahyani A, Ansari ME, Jaafari IE (2016) Traffic sign detection and recognition based on random forests. Appl Soft Comput 46:805–815

    Article  Google Scholar 

  9. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

    Article  MathSciNet  Google Scholar 

  10. Gonzalez R, Woods R (2002) Digital image processing. Pearson Education, Upper Saddle River, NJ, pp 572–580

    Google Scholar 

  11. Ho T (1995) Random decision forests. Proc 3rd Int Conf Doc Anal Recogn: 278–282

  12. Joshi A et al. (2015) A random forest approach to segmenting and classifying gestures. IEEE Int Conf Autom Face Gesture Recogn 1

  13. Liu G, Li L, Liu B (2015) Study on recognition method of adhering bars based on support vector machine. Int J Sign Process Image Recogn Patt Recogn 8(9):363–370

    Google Scholar 

  14. Mistry P, Neagu D, Trundle PR, Vessey JD (2016) Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology. Soft Comput 20(8):2967–2979

    Article  Google Scholar 

  15. Nie Z et al (2016) A novel algorithm of rebar counting on Conveyor Belt based on machine vision. J Inf Hiding Multimed Sign Process 7(2):425–437

    Google Scholar 

  16. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst, Man, Cybernet SMC-9:62–66

    Article  Google Scholar 

  17. Parvin B, Yang Q, Han J, Chang H, Rydberg B, Barcellos-Hoff MH (Mar. 2007) Iterative voting for inference of structural saliency and characterization of subcellular events. IEEE Trans Image Process 16(3):615–623

    Article  MathSciNet  Google Scholar 

  18. Powers D (2011) Evaluation: from precision, recall and F-measure to ROC, Informedness, Markedness and correlation. J Mach Learn Technol 2(1):37–63

    MathSciNet  Google Scholar 

  19. Shapiro L, Stockman G (2001) Computer vision. Prentice Hall PTR, Upper Saddle River, NJ

    Google Scholar 

  20. Zhang D et al. (2008) Bar section image enhancement and positioning method in on-line steel bar counting and automatic separating system. 2008 Congress Image Sign Process: 319–323

  21. Zhao J et al. (2016) Design of real-time steel bars recognition system based on machine vision. 8th Intell Human-Mach Syst Cybernet

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2017R1C1B5075856).

Author information

Authors and Affiliations

  1. Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, South Korea

    Jae Hwan Lee & Sang Oh Park

Authors
  1. Jae Hwan Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sang Oh Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sang Oh Park.

Additional information

The original version of this article was revised: The captured corresponding author was incorrect. The correct corresponding author is Sang Oh Park.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, J.H., Park, S.O. Machine learning-based automatic reinforcing bar image analysis system in the internet of things. Multimed Tools Appl 78, 3171–3180 (2019). https://doi.org/10.1007/s11042-018-5984-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-5984-7

Keywords

Search

Navigation