Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 3, pp 3171–3180 | Cite as

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

  • Jae Hwan Lee
  • Sang Oh ParkEmail author
Article
  • 123 Downloads

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.

Keywords

Reinforcing bar Machine learning Image analysis Internet of things Quantity management 

Notes

Acknowledgements

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

References

  1. 1.
    Achanta R et al. (2010) SLIC Superpixels. Technical report 149300 EPFLGoogle Scholar
  2. 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):349–355Google Scholar
  3. 3.
    Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  4. 4.
    Brunelli R (2009) Template matching techniques in computer vision: theory and practice. Wiley, Hoboken, NJCrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. 6.
    Davies ER (1988) A modified Hough scheme for general circle location. Pattern Recogn Lett 7(1):37–44CrossRefGoogle Scholar
  7. 7.
    Dietterich TG (2002) Ensemble learning. The handbook of brain theory and Neural NetworkGoogle Scholar
  8. 8.
    Ellahyani A, Ansari ME, Jaafari IE (2016) Traffic sign detection and recognition based on random forests. Appl Soft Comput 46:805–815CrossRefGoogle Scholar
  9. 9.
    Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874MathSciNetCrossRefGoogle Scholar
  10. 10.
    Gonzalez R, Woods R (2002) Digital image processing. Pearson Education, Upper Saddle River, NJ, pp 572–580Google Scholar
  11. 11.
    Ho T (1995) Random decision forests. Proc 3rd Int Conf Doc Anal Recogn: 278–282Google Scholar
  12. 12.
    Joshi A et al. (2015) A random forest approach to segmenting and classifying gestures. IEEE Int Conf Autom Face Gesture Recogn 1Google Scholar
  13. 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):363–370Google Scholar
  14. 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):2967–2979CrossRefGoogle Scholar
  15. 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):425–437Google Scholar
  16. 16.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst, Man, Cybernet SMC-9:62–66CrossRefGoogle Scholar
  17. 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):615–623MathSciNetCrossRefGoogle Scholar
  18. 18.
    Powers D (2011) Evaluation: from precision, recall and F-measure to ROC, Informedness, Markedness and correlation. J Mach Learn Technol 2(1):37–63MathSciNetGoogle Scholar
  19. 19.
    Shapiro L, Stockman G (2001) Computer vision. Prentice Hall PTR, Upper Saddle River, NJGoogle Scholar
  20. 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: 319–323Google Scholar
  21. 21.
    Zhao J et al. (2016) Design of real-time steel bars recognition system based on machine vision. 8th Intell Human-Mach Syst CybernetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018
corrected publication July/2018

Authors and Affiliations

  1. 1.Chung-Ang UniversitySeoulSouth Korea

Personalised recommendations