Recognizing wheels with a laser to calculate the deformation of tires
DOI:
https://doi.org/10.15587/2312-8372.2018.123441Keywords:
Canny edge detector', Sobel differential operator, computer vision, median filter, Hough transformationsAbstract
The object of research is a system for recognizing wheels using a laser to calculate the deformation of tires. The main problem of this research, for the solution of which it is necessary to recognize the vehicle wheels with the help of laser illumination, with the subsequent restoration of their three-dimensional surfaces is the calculation of the coefficient of deformation of the vehicle wheels.
In the course of the study, the vehicle and its laser illumination are simulated using the Unity 3D system. The recognition of the laser beam and the minimization of its pixels in the wheel area are carried out using algorithms implemented in the EmguCV library (OpenCV for .NET) with empirical parameter adjustment to achieve optimal recognition quality. The software is developed in C# programming language in Microsoft Visual Studio 2017. The quality of such recognition is checked in real conditions.
The results of the calculation are in accordance with UNECE Standards No. 30. The implemented algorithm for recognizing the laser beam in the wheel region of a vehicle with subsequent reconstruction of its three-dimensional surface is of independent value, since it can be used to detect any objects using a vertical or horizontal laser of different colors. These results can be integrated with the TPMS (Tire Pressure Monitoring System) information to determine vehicle traffic.
References
- Sonka, M., Hlavac, V., Boyler, R. (2014). Image Processing, Analysis, and Machine Vision. Stamford: Cengage Learning. Available at: https://www.researchgate.net/profile/Roger_Boyle/publication/220695728_Image_processing_analysis_and_and_machine_vision_3_ed/links/5553203108ae980ca606d93c/Image-processing-analysis-and-and-machine-vision-3-ed.pdf
- Szeliski, R. (2010). Computer Vision Algorithms and Applications. London: Springer-Verlag. Available at: http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf
- Wang, J., Cohen, M. F. (2007). Image and Video Matting: A Survey. Foundations and Trends in Computer Graphics and Vision, 3 (2), 97–175. doi:10.1561/0600000019
- OpenCV 2.4.13.4 documentation. (2017). Available at: https://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.html
- Zeeshan, А. А. (2016). A Quick Introduction To Computer Vision Using C#. Available at: http://www.c-sharpcorner.com/article/a-quick-introduction-to-computer-vision-using-c-sharp
- Вradski, G., Kaehler, А. (2008). Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly Media, 580.
- Christoudias, C. M., Georgescu, B., Meer, P. (2002). Synergism in low level vision. Proceedings of 16th International Conference on Pattern Recognition, 4. IEEE, 150–155. doi:10.1109/icpr.2002.1047421
- Fergus, R., Perona, P., Zisserman, A. (2003). Object class recognition by unsupervised scale-invariant learning. Proceedings of Computer Society Conference on Computer Vision and Pattern Recognition, 2. IEEE, 256–264. doi:10.1109/cvpr.2003.1211479
- Harzallah, H., Jurie, F., Schmid, C. (2009). Combining efficient object localization and image classification. 12th International Conference on Computer Vision. IEEE, 237–244. doi:10.1109/iccv.2009.5459257
- Kim, S., Yoon, K. J., Kweon, I. S. (2008). Object recognition using a generalized robust invariant feature and Gestalt's law of proximity and similarity. Pattern Recognition, 41 (2), 726–741. doi:10.1016/j.patcog.2007.05.014
- Kulkarni, S. R., Harman, G. (2011). Statistical learning theory: a tutorial. Wiley Interdisciplinary Reviews: Computational Statistics, 3 (6), 543–556. doi:10.1002/wics.179
- Li, X., Wu, C., Zach, C., Lazebnik, S., Frahm, J. M. (2008). Modeling and recognition of landmark image collections using iconic scene graphs. Computer Vision. ECCV, Springer Berlin Heidelberg, 427–440. doi:10.1007/978-3-540-88682-2_33
- Achler, O., Trivedi, M. (2004). Vehicle Wheel Detector using 2D Filter Banks, Accepted. International Conference on Intelligent Vehicles. doi:10.1109/ivs.2004.1336350
- Viola, P., Jones, M. J., Snow, D. (2005). Detecting Pedestrians Using Patterns of Motion and Appearance. International Journal of Computer Vision, 63 (2), 153–161. doi:10.1007/s11263-005-6644-8
- Sun, Z., Bebis, G., Miller, R. (2002). On-road vehicle detection using gab or filters and support vector machines. International Conference on Digital Signal Processing, 2, 1019–1022. doi:10.1109/icdsp.2002.1028263
- Lymarenko, Y., Tatievskyi, D. (2017). Development of the computer model of three dimensional surfaces reconstruction system. Technology audit and production reserves, 5 (2 (37)), 11–16. doi:10.15587/2312-8372.2017.111233
- Degtiareva, A., Vezhnevec, V. (2003). Preobrazovanie Hafa [Hough transform]. Komp’iuternaia grafika i mul’timedia, 1 (1). Available at: http://ict.informika.ru/ft/002407/num1degt.pdf
- Bradski, G., Kaehler, A. (2008). Learning OpenCV Computer Vision with OpenCV. Library. O’Reilly Media Publishers. Available at: http://www.bogotobogo.com/cplusplus/files/OReilly%20Learning%20OpenCV.pdf
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