Skip to main content
SpringerLink
Log in

Shadow identification and height estimation of defects by direct processing of grayscale images

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This paper presents a visual inspection system designed to identify and indirectly measure defect heights on aluminum castings. In the proposed approach, a metallic surface is illuminated at three different positions and grayscale images are captured. Unlike other methods that employ a threshold value from one image, the method shown in this article processes information from the three grayscale images to identify shadows. The data from these images is stored in matrices, and four data sets are obtained from element by element operations among the sets. The data sets exhibit a specific pattern at the position of a shadow, and it is possible to identify the pixels where it starts and ends. The method allows discriminating between actual shadows and dark spots. Height estimation was made based on the shadow’s size and they were compared to the actual height with an average error of 5.9%. The system uses a camera connected to a Jetson TX1 board, and the data is processed in parallel on a GPU to obtain results in real time.

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

Similar content being viewed by others

References

  1. Li X, Wang L, Cai N (2004) Machine-vision-based surface finish inspection for cutting tool replacement in production. Int J Prod Res 42:2279–2287

    Article  Google Scholar 

  2. Huang W, Kovacevic R (2011) A laser-based vision system for weld quality inspection. Sensors 11:506–521

    Article  Google Scholar 

  3. Bardak T, Bardak S, SÖZEN E (2017) Determination of strain distributions of solid wood and plywood in bending test by digital image correlation. Kastamonu Univ., Journal of Forestry Faculty 17:354–361

    Google Scholar 

  4. Sholts SB, Wärmländer SKTS, Flores LM, Miller KWP, Walker PL (2010) Variation in the measurement of cranial volume and surface area using 3D laser scanning technology. J Forensic Sci 55:871–876

    Article  Google Scholar 

  5. Blasco J, Aleixos N, Moltó E (2003) Machine vision system for automatic quality grading of fruit. Biosyst Eng 85:415–423

    Article  Google Scholar 

  6. Sari-Sarraf H, Goddard JS (1999) Vision system for on-loom fabric inspection. IEEE Trans Ind Appl 35:1252–1259

    Article  Google Scholar 

  7. Jabeen Q, Khan F, Hayat MN, Khan H, Jan SR, Ullah F (2016) A Survey: embedded systems supporting by different operating systems. arXiv Prepr arXiv161007899

  8. Tarabanis K, Tsai RY, Allen PK (1994) Analytical characterization of the feature detectability constraints of resolution, focus, and field-of-view for vision sensor planning. CVGIP Image Underst 59:340–358

    Article  Google Scholar 

  9. Kurada S, Bradley C (1997) A review of machine vision sensors for tool condition monitoring. Comput Ind 34:55–72

    Article  Google Scholar 

  10. Abouelela A, Abbas HM, Eldeeb H, Wahdan AA, Nassar SM (2005) Automated vision system for localizing structural defects in textile fabrics. Pattern Recogn Lett 26:1435–1443

    Article  Google Scholar 

  11. Brosnan T, Sun D-W (2004) Improving quality inspection of food products by computer vision----a review. J Food Eng 61:3–16

    Article  Google Scholar 

  12. Bardak T, Bardak S Prediction of wood density by using red-green-blue (rgb) color and fuzzy logic techniques. Journal of Polytechnic 20:979–984

  13. Zheng H, Kong LX, Nahavandi S (2002) Automatic inspection of metallic surface defects using genetic algorithms. J Mater Process Technol 125:427–433

    Article  Google Scholar 

  14. Xue-Wu Z, Yan-Qiong D, Yan-Yun L, Ai-Ye S, Rui-Yu L (2011) A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM. Expert Syst Appl 38:5930–5939

    Article  Google Scholar 

  15. Don H-S, Fu K-S, Liu CR, Lin W-C (1984) Metal surface inspection using image processing techniques. IEEE Trans Syst Man Cybern, Part C SMC-14:139–146

    Article  Google Scholar 

  16. Pernkopf F, O’Leary P (2002) Visual inspection of machined metallic high-precision surfaces. EURASIP J Adv Signal Process 2002:667–678

    Article  Google Scholar 

  17. Wang Z, Xing Q, Fu L, Sun H (2015) Realtime vision-based surface defect inspection of steel balls. Trans Tianjin Univ 21:76–82

    Article  Google Scholar 

  18. Li L, Wang Z, Pei F, Wang X (2013) Improved illumination for vision-based defect inspection of highly reflective metal surface. Chin Opt Lett 11:21102

    Article  Google Scholar 

  19. Lindner C, Arigita J, León FP, Osten W, Gorecki C, Novak E (2005) Illumination-based segmentation of structured surfaces in automated visual inspection. Illumination 5:6

    Google Scholar 

  20. Martinez SS, Vázquez CO, Garcia JG, Ortega JG (2017) Quality inspection of machined metal parts using an image fusion technique. Measurement 111:374–383

    Article  Google Scholar 

  21. Scaman ME, Economikos L (1995) Computer vision for automatic inspection of complex metal patterns on multichip modules (MCM-D). IEEE Trans Components, Packag Manuf Technol Part B 18:675–684

    Article  Google Scholar 

  22. Molleda J, Usamentiaga R, Garcia DF, Bulnes FG, Espina A, Dieye B et al (2013) An improved 3D imaging system for dimensional quality inspection of rolled products in the metal industry. Comput Ind 64:1186–1200

    Article  Google Scholar 

  23. Prados E, Faugeras O (2005) Shape from shading: a well-posed problem? Comput. Vis. Pattern Recognition. CVPR 2005. IEEE Comput. Soc. Conf., vol. 2, 2005, p. 870–7

  24. Visentini-Scarzanella M, Stoyanov D, Yang G-Z (2012) Metric depth recovery from monocular images using shape-from-shading and specularities. ICIP, Orlando, USA :25–8

  25. Hayakawa H (1994) Photometric stereo under a light source with arbitrary motion. JOSA A 11:3079–3089

    Article  Google Scholar 

  26. Sun J, Smith M, Smith L, Midha S, Bamber J (2007) Object surface recovery using a multi-light photometric stereo technique for non-Lambertian surfaces subject to shadows and specularities. Image Vis Comput 25:1050–1057

    Article  Google Scholar 

  27. Miyazaki D, Hara K, Ikeuchi K (2010) Median photometric stereo as applied to the segonko tumulus and museum objects. Int J Comput Vis 86:229–242

    Article  Google Scholar 

  28. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24:381–395

    Article  MathSciNet  Google Scholar 

  29. Harker M, O’Leary P (2013) Direct regularized surface reconstruction from gradients for industrial photometric stereo. Comput Ind 64:1221–1228

    Article  Google Scholar 

  30. Soukup D, Huber-Mörk R (2014) Convolutional neural networks for steel surface defect detection from photometric stereo images. Int Symp Vis Comput, p. 668–77

  31. Galan U, Orta P, Kurfess T, Ahuett-Garza H (2018) Surface defect identification and measurement for metal castings by vision system. MFGLET 15:5–8

    Google Scholar 

  32. Wu T-P, Tang C-K (2010) Photometric stereo via expectation maximization. IEEE Trans Pattern Anal Mach Intell 32:546–560

    Article  Google Scholar 

  33. Akanegawa M, Tanaka Y, Nakagawa M (2001) Basic study on traffic information system using LED traffic lights. IEEE Trans Intell Transp Syst 2:197–203

    Article  Google Scholar 

  34. Inoue M (1999) Method for measuring heights of bumps and apparatus for measuring heights of bumps

  35. Moreno I, Sun C-C (2008) Modeling the radiation pattern of LEDs. Opt Express 16:1808–1819

    Article  Google Scholar 

Download references

Acknowledgments

The authors gratefully acknowledge the support of the Automotive Consortium for Cyber Physical Systems at Tecnologico de Monterrey Campus Monterrey.

Author information

Authors and Affiliations

  1. Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, N.L., Mexico

    Ulises Galan, Horacio Ahuett-Garza & Pedro Orta

  2. Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Distinguished Professor with the Automotive Consortium, Monterrey, N.L., Mexico

    Thomas Kurfess

  3. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Thomas Kurfess

Authors
  1. Ulises Galan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Horacio Ahuett-Garza
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pedro Orta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas Kurfess
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ulises Galan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Galan, U., Ahuett-Garza, H., Orta, P. et al. Shadow identification and height estimation of defects by direct processing of grayscale images. Int J Adv Manuf Technol 101, 317–328 (2019). https://doi.org/10.1007/s00170-018-2933-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-018-2933-6

Keywords

Search

Navigation