Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry
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This paper focus on the classification, in real-time and under uncontrolled lighting, of fabric textures for the automotive industry. Many industrial processes have spatial constraints that limit the effective control of illumination of their vision based systems, hindering their effectiveness. The ability to overcome these problems using robust classification methods with suitable pre-processing techniques and choice of characteristics will increase the efficiency of this type of solutions with obvious production gains and thus economical. For this purpose, this paper studied and analyzed various pre-processing techniques, and selected the most appropriate fabric characteristics for the considered industrial case scenario. The methodology followed was based on the comparison of two different machine learning classifiers, ANN and SVM, using a large set of samples with a large variability of lightning conditions to faithfully simulate the industrial environment. The obtained solution shows the sensibility of ANN over SVM considering the number of features and the size of the training set, showing the better effectiveness and robustness of the last. The characteristics vector uses histogram equalization, Laws filter and Sobel filter, and multi-scale analysis. By using a correlation based method was possible to reduce the number of features used, achieving a better balanced between processing time and classification ratio.
KeywordsComputer vision Perception and recognition Fabric analyses Machine learning Uncontrolled illumination Automotive industry
- AForge.NET: Computer Vision, Artificial Intelligence, Robotics [Internet]. [cited January 7, 2014]. Available from http://www.aforgenet.com/.
- Ahmed, S. A., Dey, S., & Sarma, K. K. (2011). Image texture classification using artificial neural network (ANN). In 2011 2nd national conference on emerging trends and applications in computer science (NCETACS) (p. 14).Google Scholar
- Aleksander, I., & Morton, H. (1990). An introduction to neural computing (Vol. 3). London: Chapman and Hall.Google Scholar
- Arun, D. K. (1993). Artificial neural networks for image understanding (1st ed.). New York, NY: Wiley.Google Scholar
- Ballard, D. H., & Brown, C. M. (1982). Computer vision. London: Prentice-Hall.Google Scholar
- Ben Salem, Y., & Nasri, S. (2010). Automatic recognition of woven fabrics based on texture and using SVM (pp. 429–434). Image and Video Processing: Journal Signal.Google Scholar
- Fellows, P. J. (2009). In P. J. Fellows (Eds.), Woodhead publishing series in food science, technology and nutrition (3rd edn, pp. 1–8). Woodhead Publishing, Food Processing Technology. ISBN 9781845692162. doi: 10.1533/9781845696344.1.
- Gonzalez, R. C., & Woods, R. E. (2006). Digital image processing. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
- Haralick, R. M., Shanmugam, K., & Dinstein (1973). Its’Hak, “Textural features for image classification”. IEEE Transactions on Systems, Man and Cybernetics, SMC-3(6), 610–621.Google Scholar
- Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. In Maglogiannis, I., Karpouzis, K., Wallace, M., & Soldatos, J. (Eds.), Proceedings of the 2007 conference on emerging artificial intelligence applications in computer engineering: Real word AI systems with applications in eHealth, HCI, information retrieval and pervasive technologies (pp. 3–24). Amsterdam, The Netherlands: I.O.S. Press.Google Scholar
- Laws, K. I. (2014). Textured image segmentation. Ph.D. Thesis, University of Southern California Los Angels Image Processing Institute [cited January 4, 2014]. Available from http://www.dtic.mil/docs/citations/ADA083283.
- Loke, K.S. (2009). An approach to textile recognition, pattern recognition. In P.-Y. Yan (Ed.), ISBN: 978-953-307-014-8, InTech, doi: 10.5772/7531. Available from http://www.intechopen.com/books/pattern-recognition/an-approach-to-textile-recognition.
- MVTec HALCON 10—Highlights of version 10 [Internet]. [cited January 7, 2014]. Available from http://www.halcon.com/halcon/version10/.
- Pavelka, A. P., & Procházka, P. (2004). Algorithms for initialization of neural network weights. Tech Comput: Proc. Conf.Google Scholar
- Pinto, N., Cox, D. D., & DiCarlo, J. J. (2008). Why is real-world visual object recognition hard? PLoS Comput Biol, 4(1), e27. doi: 10.1371/journal.pcbi.0040027.
- Rocha, L. F., Malaca, P., Silva, J., Moreira, A. P., & Veiga, G. (2015). Development of a 3D model based part recognition system for industrial applications. In 2015 IEEE international conference on main challenges, industrial technology (ICIT) (pp. 3296–3301).Google Scholar
- Sanders, D., Lambert, G., Pevy, L., & Tewkesbury, G. (2009). Improving robotic welding in the shipbuilding industry through the recognition of ship-building parts by pre-locating corners in images. Budapest; 2009 [cited January 7, 2014]. Available from http://eprints.port.ac.uk/5305.
- Schwartz, W. R., & Pedrini, H. (2007). Color textured image segmentation based on spatial dependence using 3D co-occurrence matrices and markov random fields. In 15th international conference in Central Europe on computer graphics, visualization and computer vision, Plzen, Czech Republic (pp. 81–87).Google Scholar
- Sharma, M., & Singh, S. (2001). Evaluation of texture methods for image analysis. Intelligent Information Systems Conference, The Seventh Australian and New Zealand, 2001, 11721.Google Scholar
- Shenbagavalli, R., & Ramar, K. (2011). Classification of soil textures based on laws features extracted from preprocessing images on sequential and random windows. Bonfring International Journal of Advances in Image Processing, 1, 15–18.Google Scholar
- Theodoridis, S., & Koutroumbas, K. (2009). Pattern recognition (4th ed.). Boston, MA: Academic Press.Google Scholar