Abstract
The surface crack detection of rubber insulator is an essential part of its quality inspection. Aiming at the problem of low efficiency and complicated operation of manual inspection, an automatic surface crack detection algorithm of rubber insulator based on local threshold algorithm is presented in this paper. Firstly, the source image is filtered by a Dimension-increased Bilateral Filter to weaken the effects of noise and the inherent texture of the rubber surface. Then, the filtered image is segmented by Sauvola Local Threshold to separate the cracks from the background. Subsequently, an algorithm combined morphological processing with Seed Filling algorithm is applied to connect the discontinuous cracks. Finally, the real cracks are located by measuring the connected domain and using the distance threshold. The experimental results show that the proposed method can effectively remove the background interference and accurately locate the cracks, with an accuracy of 94.3%.
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References
Carlos, J., Amado, Q., Ross, P.G., et al.: Evaluation of elastomeric heat shielding materials as insulators for solid propellant rocket motors: a short review. Open Chem. 18(1), 1452–1467 (2020)
Wei, H., Jiang, D.: Research on the depth inspection method of non-contact rubber surface defects. Chin. Sci. Technol. J. Database (Full-text Ed.) Eng. Technol. 00308–00310. (in Chinese)
Xuewu, Z., Yanqiong, D., et al.: Surface defects inspection of copper strips based on vision bionics. J. Image Graph. 16(04), 593–599 (2011). (in Chinese)
Li, J., Hu, H., Shen, J.: Research on micro defect detection method of small magnetic tile surface based on machine vision. Mech. Electr. Eng. 184(002), 117–123 (2019). (in Chinese)
Paris, S., Durand, F.A.: Fast approximation of the bilateral filter using a signal processing approach. Int. J. Comput. Vis. 81(1), 24–52 (2009)
Lazzara, G., Geraud, T.: Efficient multiscale sauvola’s binarization. Int. J. Doc. Anal. Recogn. 17(2), 105–123 (2014)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Posts and Telecom Press, Thomson Brooks/Cole, Beijing (2002)
Lasheng, Yu., Deyao, S.: A refinement of scanning line seed filling algorithm. Comput. Eng. 10, 70–72 (2003). (in Chinese)
He, L.F., Chao, Y.Y., Suzuki, K., et al.: Fast connected-component labeling. Pattern Recogn. J. Pattern Recogn. Soc. 42(9), 1977–1987 (2009)
Chunjian, H., Xuemei, X., Ying, C.: Feature extraction of workpiece circular arc contour based on sobel operator. J. Laser Optoelectron. Prog. 55(002), 233–240 (2018). (in Chinese)
Xiao, M., Li, J., Peng, Y.: A detection algorithm for the inner wall crack of ceramic bottles. Chin. Ceram. Ind. 27(04), 43–48 (2020). (in Chinese)
Jiawei, Z., Xing, F., He, C., et al.: Detection of crack in drying wood based on image processing. Heilongjiang Sci. Technol. Inf. (016), 14–16 (2019). (in Chinese)
Acknowledgement
This research is partially supported by the key research project of the Ministry of Science and Technology (Grant No. 2018YFB1306802), the National Natural Science Foundation of China (Grant No. 51975344) and China Postdoctoral Science Foundation (Grant No. 2019M662591).
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Wang, J., Li, C., Zhang, X., Jiang, Y. (2021). Surface Crack Detection of Rubber Insulator Based on Machine Vision. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13014. Springer, Cham. https://doi.org/10.1007/978-3-030-89098-8_17
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DOI: https://doi.org/10.1007/978-3-030-89098-8_17
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