Abstract
The quality of uni-directional tape in its production process is affected by environmental conditions like temperature and production speed. In this paper, computer vision algorithms on the scanned images are needed to be used in this context to detect and classify tape damages during the manufacturing procedure. We perform a comparative study among famous feature descriptors for fault candidate generation, then propose own features for fault detection. We investigate various machine learning techniques to find best model for the classification problem. The empirical results demonstrate the high performance of the proposed system and show preference of random forest and canny edges for classifier and feature generator respectively.
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References
Kropka, M., Muehlbacher, M., Neumeyer, T., Altstaedt, V.: From UD-tape to final part-a comprehensive approach towards thermoplastic composites. Procedia CIRP 66, 96–100 (2017)
Berger, D., Egloff, A., Summa, J., Schwarz, M., Lanza, G., Herrmann, H.-G.: Conception of an eddy current in-process quality control for the production of carbon fibre reinforced components in the rtm process chain. Procedia CIRP 62, 39–44 (2017)
Grosse, C.U., et al.: Comparison of NDT techniques to evaluate CFRP-results obtained in a MAIzfp round robin test. In 19th World Conference on Non-Destructive Testing (WCNDT), Munich/Germany (2016)
Aymerich, F., Meili, S.: Ultrasonic evaluation of matrix damage in impacted composite laminates. Compos. Part B: Eng. 31(1), 1–6 (2000)
Vikram Gopal, C.S.L. Continuous fiber thermoplastic composites (2015)
Liu, H., Liu, S., Liu, Z., Mrad, N., Dong, H.: Prognostics of damage growth in composite materials using machine learning techniques. In: 2017 IEEE International Conference on Industrial Technology (ICIT), pp. 1042–1047. IEEE (2017)
Kitanovski, I., Jankulovski, B., Dimitrovski, I., and Loskovska, S. Comparison of feature extraction algorithms for mammography images. In: 2011 4th International Congress on Image and Signal Processing, vol. 2, pp. 888–892. IEEE (2011)
Erazo-Aux, J., Loaiza-Correa, H., Restrepo-Giron, A.: Histograms of oriented gradients for automatic detection of defective regions in thermograms. Appl. Opt. 58(13), 3620–3629 (2019)
Han, J., Yang, S., Lee, B.: A novel 3-d color hisogram equalization method with uniform 1-d gray scale histogram. IEEE Trans. Image Process. 20(2), 506–512 (2011). ISSN 1941-0042
Wu, X.Y., Yang, L., Li, S.B., Xu, P.: An interactive video foreground segmentation system based on modeling and dynamic graph cut algorithm. In: Advanced Materials Research, vol. 532, pp. 1770–1774. Trans Tech Publications (2012)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Ozturk, S., Bayram, A.: Comparison of HOG, MSER, SIFT, FAST, LBP and CANNY features for cell detection in histopathological images. HELIX 8(3), 3321–3325 (2018)
El-Gayar, M., Soliman, H., et al.: A comparative study of image low level feature extraction algorithms. Egypt. Inf. J. 14(2), 175–181 (2013)
Amaricai, A., Gavriliu, C.-E., Boncalo, O.: An FPGA sliding window-based architecture harris corner detector. In 2014 24th International Conference on Field Programmable Logic and Applications (FPL), pp. 1–4. IEEE (2014)
Jain, R., Rangachar Kasturi, B.S.: Machine Vision. McGraw-Hill Inc., New York (1995). ISBN 0-07-032018-7
Gan, K., et al.: Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Acta Orthop. 90, 1–12 (2019)
García, V., Mollineda, R.A., Sánchez, J.S., Alejo, R., Sotoca, J.M.: When overlapping unexpectedly alters the class imbalance effects. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007, Part II. LNCS, vol. 4478, pp. 499–506. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72849-8_63
Wålinder, A.: Evaluation of logistic regression and random forest classification based on prediction accuracy and metadata analysis (2014)
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Devagekar, S., Delforouzi, A., Plöger, P.G. (2021). Fault Detection in Uni-Directional Tape Production Using Image Processing. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_52
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DOI: https://doi.org/10.1007/978-3-030-68799-1_52
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