Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Real time vision-based measurements for quality control of industrial rods on a moving conveyor

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

Abstract

This work proposes a fully automated approach for vision-based quality control of manufactured metal rods. The proposed approach is able to detect the main axis of the rod and calculate its curvature, versus specifications. The proposed algorithm utilizes video acquired in real time by a single mono-ocular USB camera. A signal processing module identifies in real time the video frame that images the rod at the appropriate position on the conveyor. Initialization of the algorithm can take place either manually, or by utilizing the calibration of the camera. Concurrently, the image processing module estimates the curvature of the rod using its medial axis, to classify the rod as normal or defect. Initial results show that the proposed algorithm can operate in real time with very high accuracy under controlled illumination conditions and backgrounds. This methodology is capable of processing video at 30 frames per second, using a general purpose laptop.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Bahlmann C, Heidemann G, Ritter H (1999) Artificial neural networks for automated quality control of textile seams. Pattern Recogn 32(6):1049–1060

    Article  Google Scholar 

  2. Bosché F (2010) Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction. Adv Eng Inform 24(1):107–118

    Article  Google Scholar 

  3. Bresenham JE (1965) Algorithm for computer control of digital plotter. IBM Syst J 4:25–30

    Article  Google Scholar 

  4. Fraser CS (1997) Innovations in automation for vision metrology systems. Photogramm Rec 15(90):901–911

    Article  Google Scholar 

  5. Gonzalez RC, Woods RE (1992) Digital Image Processing. Addison-Wesley 5:11–15 Chap. 2

    Google Scholar 

  6. Jamshidi J, Kayani A, Iravani P, Maropoulos PG, Summers MD (2010) Manufacturing and assembly automation by integrated metrology systems for aircraft wing fabrication. Proc Inst Mech Eng B J Eng Manuf 224(1):25–36

    Article  Google Scholar 

  7. Jiménez AR, Ceres R, Pons JL (2000) A vision system based on a laser range-finder applied to robotic fruit harvesting. Mach Vis Appl 11(6):321–329

    Article  Google Scholar 

  8. Jurca MC (1993) Process for quality control of laser beam welding and cutting. U.S. patent no. 5,272,312. U.S. patent and trademark office, Washington, DC

  9. Kottari K, Delibasis K, Plagianakos V. (2016): Real time measurements for quality control of industrial rod manufacturing. In: 2016 I.E. international conference on imaging systems and techniques (IST). IEEE Press, pp 423–428. doi:10.1109/IST.2016.7738263

  10. Lam L, Lee SW, Suen CY (1992) Thinning methodologies-a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 14(9):869–885

    Article  Google Scholar 

  11. Liu YC, Hsu YL, Sun YN, Tsai SJ, Ho CY, Chen CM (2010) A computer vision system for automatic steel surface inspection. In: 5th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE Press, pp 1667–1670. doi:10.1109/ICIEA.2010.5515197

  12. Malamas EN, Petrakis EG, Zervakis M, Petit L, Legat JD (2003) A survey on industrial vision systems, applications and tools. Image Vis Comput 21(2):171–188

    Article  Google Scholar 

  13. Oppenheim AV, Schafer RW (2013) Discrete-time signal processing: Pearson new International Edition. Pearson Higher Ed. Boston, MA

  14. Packianather MS, Drake PR (2000) Neural networks for classifying images of wood veneer. Part 2. Int J Adv Manuf Technol 16(6):424–433

    Article  Google Scholar 

  15. Park C, Choi S, Won S (2010) Vision-based inspection for periodic defects in steel wire rod production. Opt Eng 49(1):017202

    Article  Google Scholar 

  16. Samet H, Tamminen M (1988) Efficient component labeling of images of arbitrary dimension represented by linear bintrees. IEEE Trans Pattern Anal Mach Intell 10(4):579–586

    Article  Google Scholar 

  17. Stojanovic R, Mitropulos P, Koulamas C, Karayiannis Y, Koubias S, Papadopoulos G (2001) Real-time vision-based system for textile fabric inspection. Real-Time Imaging 7(6):507–518

    Article  MATH  Google Scholar 

  18. Suresh BR, Fundakowski RA, Levitt TS, Overland JE (1983) A real-time automated visual inspection system for hot steel slabs. IEEE Trans Pattern Anal Mach Intell 5(6):563–572

    Article  Google Scholar 

  19. Tsai R (1987) A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal on Robotics and Automation 3(4):323–344

    Article  Google Scholar 

  20. Wulf O, Wagner B (2003) Fast 3D scanning methods for laser measurement systems. In: 14th international conference on control systems and computer science (CSCS14). Editura Politehnica Press, pp 2–5

  21. Yun JP, Choi S, Seo B, Kim SW (2008) Real-time vision-based defect inspection for high-speed steel products. Opt Eng 47(7):077204

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece

    K. Kottari, K. Delibasis & V. Plagianakos

Authors
  1. K. Kottari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Delibasis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. Plagianakos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Kottari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kottari, K., Delibasis, K. & Plagianakos, V. Real time vision-based measurements for quality control of industrial rods on a moving conveyor. Multimed Tools Appl 77, 9307–9324 (2018). https://doi.org/10.1007/s11042-017-4891-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4891-7

Keywords

Search

Navigation