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Generation of differential topographic images for surface inspection of long products

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Abstract

The current manufacturing industries need efficient quality control systems to ensure their products are free of defects. In most cases, surface inspection is carried out by automatic systems that process 2D images which lack measurable information such as the height or depth of the surface defects. An alternative technology for surface inspection is laser scanning. Using this technique, a 3D representation of a product can be generated and therefore, defects can be easily measured. This paper proposes a real-time algorithm to generate differential topographic images of the surface of a product using laser scanning. The images generated by the proposed method are a flattened representation of the surface of the product which compare it to a perfect-shaped product. In these images, the volumetric defects can be easily segmented and measured using computer vision techniques to fulfill the requirements of the international standards of quality. The proposed algorithm is tested on 500,000 profiles meeting the constraints of real time.

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References

  1. Besl, P.J., McKay, N.D.: A method for registration of 3-d shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  2. Deutschl, E., Gasser, C., Niel, A., Werschonig, J.: Defect detection on rail surfaces by a vision based system. In: Intelligent Vehicles Symposium, 2004 IEEE, pp. 507–511. IEEE, (2004)

  3. Duran, O., Althoefer, K., Seneviratne, L.D.: Automated pipe defect detection and categorization using camera/laser-based profiler and artificial neural network. IEEE Trans. Autom. Sci. Eng. 4(1), 118–126 (2007)

    Article  Google Scholar 

  4. Garrett, T., Radkowski, R., Sheaffer, J.: Gpu-accelerated descriptor extraction process for 3d registration in augmented reality. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3085–3090. (2016)

  5. Gautier, Q., Shearer, A., Matai, J., Richmond, D., Meng, P., Kastner, R.: Real-time 3d reconstruction for fpgas: a case study for evaluating the performance, area, and programmability trade-offs of the altera opencl sdk. In: 2014 International Conference on Field-Programmable Technology (FPT), pp. 326–329. (Dec 2014)

  6. Grant, W.S., Voorhies, R.C., Itti, L.: Finding planes in lidar point clouds for real-time registration. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4347–4354. (2013)

  7. Hoła, J., Sadowski, Ł., Reiner, J., Stach, S.: Usefulness of 3d surface roughness parameters for nondestructive evaluation of pull-off adhesion of concrete layers. Constr. Build. Mater. 84, 111–120 (2015)

    Article  Google Scholar 

  8. Ikeda, O., Duan, Y.: Color photometric stereo for albedo and shape reconstruction. In: Applications of Computer Vision, 2008. WACV 2008. IEEE Workshop on, pp. 1–6. IEEE, (2008)

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

    Article  Google Scholar 

  10. Molleda, J., Usamentiaga, R., García, D.F., Bulnes, F.G.: Real-time flatness inspection of rolled products based on optical laser triangulation and three-dimensional surface reconstruction. J. Electron. Imaging 19(3), 031206–031206 (2010)

    Article  Google Scholar 

  11. Molleda, J., Usamentiaga, R., Millara, Á. F., García, D.F., Manso, P., Suárez, C.M., García, I.: A profile measurement system for rail manufacturing using multiple laser range finders. In: Industry Applications Society Annual Meeting, 2015 IEEE, pp. 1–8. IEEE, (2015)

  12. Qian, Y., Cao, P., Yin, W., Dai, F., Hu, F., Yan, Z.: Calculation method of surface shape feature of rice seed based on point cloud. Comput. Electron. Agric. 142, 416–423 (2017)

    Article  Google Scholar 

  13. Santur, Y., Karaköse, M., Akin, E.: A new rail inspection method based on deep learning using laser cameras. In: Artificial Intelligence and Data Processing Symposium (IDAP), 2017 International, pp. 1–6. IEEE, (2017)

  14. Seçil, S., Turgut, K., Parlaktuna, O., Özkan, M.: 3-d visualization system for geometric parts using a laser profile sensor and an industrial robot. In: Robotics and Manufacturing Automation (ROMA), 2014 IEEE International Symposium on, pp. 160–165. IEEE, (2014)

  15. Sharifzadeh, S., Biro, I., Lohse, N., Kinnell, P.: Abnormality detection strategies for surface inspection using robot mounted laser scanners. Mechatronics 51, 59–74 (2018)

    Article  Google Scholar 

  16. Skinner, K.A., Johnson-Roberson, M.: Towards real-time underwater 3d reconstruction with plenoptic cameras. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2014–2021. (2016)

  17. 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(02), 329–347 (2015)

    Article  Google Scholar 

  18. Woodham, R.J.: Photometric method for determining surface orientation from multiple images. Opt. Eng. 19(1), 191139 (1980)

    Article  Google Scholar 

  19. Xiong, Z., Li, Q., Mao, Q., Zou, Q.: A 3d laser profiling system for rail surface defect detection. Sensors 17(8), 1791 (2017)

    Article  Google Scholar 

  20. Ye, C., Acikgoc, S., Pendrigh, S., Riley, E., DeJonj, M.J.: Mapping deformations and inferring movements of masonry arch bridges using point cloud data. Eng. Struct. 173, 530–545 (2018)

    Article  Google Scholar 

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Acknowledgements

This work has been partially funded by the project TIN2014-56047-P of the Spanish National Plan for Research, Development and Innovation.

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Authors and Affiliations

  1. University of Oviedo, Gijón, Spain

    F. J. delaCalle, Daniel García & Rubén Usamentiaga

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  1. F. J. delaCalle
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  2. Daniel García
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  3. Rubén Usamentiaga
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Correspondence to F. J. delaCalle.

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delaCalle, F.J., García, D. & Usamentiaga, R. Generation of differential topographic images for surface inspection of long products. J Real-Time Image Proc 17, 967–980 (2020). https://doi.org/10.1007/s11554-018-0844-2

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