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Real-time filtering on parallel SIMD architectures for automated quality inspection

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Abstract

Surface metrology in automated quality inspection is a field, among many others, affected by noise and thus requiring filtering. In surface metrology, filtering is required to remove undesired information from data in order to extract surface features and relevant properties necessary for quality control. Moreover, filtering requires immediate results, while the product is being manufactured. This way, quick correcting actions can be directly applied to solve possible manufacturing issues. This work proposes different strategies to filter height maps in real-time acquired using laser profilers, the most widely used inspection method in industrial applications. Different models to apply the filtering operations are considered, particularly assessing different alternatives to store previous samples in memory, which are required for data filtering. FIFO, double FIFO, circular and double circular buffers are evaluated. Furthermore, CPU parallelism, SIMD instructions and cache-line friendly data structures are analyzed. The proposed methods are extremely efficient, capable of filtering laser profiles at extremely high acquisition rates. The proposed methods are designed for real-time surface metrology, but they are very likely to find potential applications in different areas. The filters are compared in terms of accuracy and speed, including other well-known filters such as the spline filter. Tests analyze execution time, including cache efficiency and filtering accuracy. Results with synthetic data and real data obtained from steel strips show excellent performance, providing accurate results at very high speeds.

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References

  1. Conroy, M., Armstrong, J.: A Comparison of Surface Metrology Techniques. Journal of Physics: Conference Series, vol. 13, p. 458. IOP Publishing, Bristol (2005)

    Google Scholar 

  2. Usamentiaga, R., Garcia, D.F., de la Calle Herrero, F.J.: Real-time inspection of long steel products using 3-d sensors: calibration and registration. IEEE Trans. Ind. Appl. 54(3), 2955–2963 (2018)

    Article  Google Scholar 

  3. Whitehouse, D.J.: Handbook of Surface Metrology. CRC Press, Boca Raton (1994)

    Google Scholar 

  4. Jiang, X., Scott, P.J., Whitehouse, D., Blunt, L.: Paradigm shifts in surface metrology. Part I. Historical philosophy. Proc. R. Soc. A Math. Phys. Eng. Sci. 463(2085), 2049–2070 (2007)

    Google Scholar 

  5. Mathia, T., Pawlus, P., Wieczorowski, M.: Recent trends in surface metrology. Wear 271(3–4), 494–508 (2011)

    Article  Google Scholar 

  6. Sansoni, G., Trebeschi, M., Docchio, F.: State-of-the-art and applications of 3d imaging sensors in industry, cultural heritage, medicine, and criminal investigation. Sensors 9(1), 568–601 (2009)

    Article  Google Scholar 

  7. Usamentiaga, R., Garcia, D.: Multi-camera calibration for accurate geometric measurements in industrial environments. Measurement 134, 345–358 (2019)

    Article  Google Scholar 

  8. Salvi, J., Fernandez, S., Pribanic, T., Llado, X.: A state of the art in structured light patterns for surface profilometry. Pattern Recognit. 43(8), 2666–2680 (2010)

    Article  Google Scholar 

  9. Petrie, G., Toth, C.K.: Introduction to Laser Ranging, Profiling, and Scanning. Topographic Laser Ranging and Scanning, pp. 1–28. CRC Press, Boca Raton (2018)

    Book  Google Scholar 

  10. Usamentiaga, R., Daniel, G., DelaCalle, F.J.: Geometric reconstruction and measurement of long steel products using 3d sensors in real-time. IEEE Trans. Ind. Appl. 55, 5476 (2019)

    Article  Google Scholar 

  11. delaCalle, F.J., García, D., Usamentiaga, R. (2019) Generation of differential topographic images for surface inspection of long products. J. Real-Time Image Process. https://doi.org/10.1007/s11554-018-0844-2

  12. Muralikrishnan, B., Raja, J.: Computational Surface and Roundness Metrology. Springer, Berlin (2008)

    Google Scholar 

  13. Leach, R.: Characterisation of Areal Surface Texture. Springer, Berlin (2013)

    Book  Google Scholar 

  14. Taubin, G., Zhang, T., Golub, G.: “Optimal surface smoothing as filter design. In: European Conference on Computer Vision, pp. 283–292. Springer (1996)

  15. Usamentiaga, R., García, D.F., Molleda, J.: Efficient registration of 2d points to cad models for real-time applications. J. Real-Time Image Process. 15(2), 329–347 (2018)

    Article  Google Scholar 

  16. Kehtarnavaz, N., Gamadia, M.: Real-time image and video processing: from research to reality. Synth. Lect. Image Video Multimed. Process. 2(1), 1–108 (2006)

    Article  Google Scholar 

  17. Lou, S., Jiang, X., Scott, P.J.: Correlating motif analysis and morphological filters for surface texture analysis. Measurement 46(2), 993–1001 (2013)

    Article  Google Scholar 

  18. Young, I.T., Van Vliet, L.J.: Recursive implementation of the gaussian filter. Signal Process. 44(2), 139–151 (1995)

    Article  Google Scholar 

  19. Brinkmann, S.: Accessing roughness in three-dimensions using gaussian regression filter. In.t J. Mach. Tools Manuf. 41, 2153–2161 (2001)

    Article  Google Scholar 

  20. Krystek, M.: Discrete l-spline filtering in roundness measurements. Measurement 18(2), 129–138 (1996)

    Article  MathSciNet  Google Scholar 

  21. Fu, S., Muralikrishnan, B., Raja, J.: Engineering surface analysis with different wavelet bases. J. Manuf. Sci. Eng. 125(4), 844–852 (2003)

    Article  Google Scholar 

  22. International Organization for Standardization: ISO 25178–2:2012 Geometrical product specifications. ISO, Standard (2009)

  23. American Society of Mechanical Engineers: B46.1-2009 Surface Texture (Surface Roughness, Waviness, and Lay). ASME, Standard (2009)

  24. International Organization for Standardization: ISO 16610: Geometrical product specifications (GPS) - Filtration. ISO, Standard (2002)

  25. Smith, S.W.: The Scientist and Engineer’s Guide to Digital Signal Processing. California Technical Publication, San Diego (1997)

    Google Scholar 

  26. Zhang, H., Ott, D., Song, J., Tong, M., Chu, W.: A simple and fast spline filtering algorithm for surface metrology. J. Res. Natl. Inst. Stand. Technol. 120, 129 (2015)

    Article  Google Scholar 

  27. Goto, T., Miyakura, J., Umeda, K., Kadowaki, S., Yanagi, K.: A robust spline filter on the basis of l2-norm. Precis. Eng. 29(2), 157–161 (2005)

    Article  Google Scholar 

  28. Su, Y., Xu, Z., Jiang, X.: GPGPU-based gaussian filtering for surface metrological data processing. In: 12th International Conference Information Visualisation, pp. 94–99. IEEE (2008)

  29. Zhang, C., Xu, Y., He, J., Lu, J., Lu, L., Xu, Z.: Multi-gpus gaussian filtering for real-time big data processing. In: 2016 10th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), pp. 231–236. IEEE (2016)

  30. Luo, H., Gai, X., Chang, Z., Hui, B.: A real-time multi-scale 2d gaussian filter based on FPGA. In: International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, vol. 9301, p. 930104. International Society for Optics and Photonics (2014)

  31. Lustig, D., Martonosi, M.: Reducing GPU offload latency via fine-grained CPU-GPU synchronization. In: IEEE 19th International Symposium on High Performance Computer Architecture (HPCA), pp. 354–365. IEEE (2013)

  32. Honzátko, D., Kruliš, M.: Accelerating block-matching and 3D filtering method for image denoising on GPUs. J. Real-Time Image Process. 16(6), 2273–2287 (2019)

    Article  Google Scholar 

  33. Usamentiaga Fernández, R., Molleda Meré, J., García Martínez, D.F., González Bulnes, F.: Software architecture for time-constrained machine vision applications. J. Electron. Imaging 22(1), 013001 (2013)

    Article  Google Scholar 

  34. Mattson, T.G., Sanders, B., Massingill, B.: Patterns for Parallel Programming. Pearson Education, London (2004)

    MATH  Google Scholar 

  35. Lomont, C.: Introduction to Intel advanced vector extensions. Intel White Paper, pp. 1–21 (2011)

  36. Usamentiaga, R., Molleda, J., Garcia, D.F., Bulnes, F.G., Entrialgo, J., Alvarez, C.M.S.: Flatness measurement using two laser stripes to remove the effects of vibrations. IEEE Trans. Ind. Appl. 51(5), 4297–4304 (2015)

    Article  Google Scholar 

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  1. Department of Computer Science and Engineering, University of Oviedo, Campus de Viesques, 33204, Gijón, Asturias, Spain

    R. Usamentiaga

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  1. R. Usamentiaga
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Usamentiaga, R. Real-time filtering on parallel SIMD architectures for automated quality inspection. J Real-Time Image Proc 18, 127–141 (2021). https://doi.org/10.1007/s11554-020-00954-3

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