Abstract
This work proposes a system to detect visual defects in an optical fiber. Fibers of different types and with different simulated deformations were used, looking for an approximation of a real case of defect in an optical fiber. Some continuous fiber patterns were detected in images captured with a microscopic camera. The identification of these patterns was searched using different image processing techniques, such as edge detection, line detection and feature descriptors. In order to classify images of the fibers in good and defective ones, a fuzzy classifier was used. Experimental results of the algorithm are shown and is demonstrated that the proposed method helps to detect defects and classify optical fibers.
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References
Ziemann, O., Zamzow, P., Daum, W.: POF Handbook: Optical Short-Range Transmission Systems, vol. 1, pp. 1–2. Springer Science & Business, Heidelberg (2008). https://doi.org/10.1007/978-3-540-76629-2. Nar
Jay, J.A.: An overview of macrobending and microbending of optical fibers. White Paper WP1212, Corning (2010)
Leal-junior, A., Frizera, A., Pontes, M.: Sensitive zone parameters and curvature radius evaluation for polymer optical fiber curvature sensors. Opt. Laser Technol. 100, 272–281 (2018)
Shahraray, B., Schmidt, A.T., Palmquist, J.M.: Defect detection, classification and quantification in optical fiber connectors. In: IAPR Workshop on Machine Vision Applications, Tokyo (1990)
Sinha, S.K., Fieguth, P.W.: Morphological segmentation and classification of underground pipe images. Mach. Vis. Appl. 17, 45–56 (2006)
Hagele, C., Neto, A.F., Pontes, M.J.: Polymer optical fiber curvature measuring technique based on speckle pattern image processing. In: Simpósio Brasileiro de Automação Inteligente, Natal-Brazil (2015)
Xiao-rong, C., Yuan, C., Chuan-li, X.: Research on the Algorithm about Optical Fiber Parameters Measurement. TELKOMNIKA 11(11), 6693–6698 (2013)
Schneider, G.A.: Segmentation and extraction of defects characteristics in radiographic images of ducts and soldering cords. 154 f. Master thesis, Electrical Engineering, CEFET-PR, Curitiba (2005)
Alwayns, V.: Optical Network Design and Implementation. Cisco Press, Indianapolis (2004)
The Fiber Optic Association, Inc.: Guide to Fiber Óptics & Premises Cabling: Micro-Bending (2013)
Curran, M., Shirk, B.: Basics of Fiber Optics, Whitepapers
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-Up robust features (SURF). Comput. Vis. Image Underst. 100(3), 346–359 (2008)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 10(22), 761–767 (2004)
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Mafalda, M., Welfer, D., De Souza Leite Cuadros, M.A., Gamarra, D.F.T. (2018). Image Processing Algorithm to Detect Defects in Optical Fibers. In: Barreto, G., Coelho, R. (eds) Fuzzy Information Processing. NAFIPS 2018. Communications in Computer and Information Science, vol 831. Springer, Cham. https://doi.org/10.1007/978-3-319-95312-0_21
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DOI: https://doi.org/10.1007/978-3-319-95312-0_21
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