Abstract
Expanded thermoplastic polyurethane (E-TPU) midsole is an emerging production. There is little industrial research on its surface defect detection in the modern society. The detection of E-TPU midsole is a new and developing field. However, the detection of E-TPU products still relies on manual detection, which is not only of high cost but is also not satisfied with the requirement of real-time online monitoring in the current industrial. Therefore, there is a surface defect detection method based on machine vision proposed in this paper. First, a second-time difference method is used to weaken the influence of background light coming from product images when it is collected and extract defective parts potentially. Then, the differences of adjacent elements are calculated by the second-order difference method, which is adopted to further test convexity–concavity and selected the appropriate threshold to identify whether there are any quality issues with these suspicious parts. In order to improve the detection effect in monitoring the equipment situation at any moment, we use MATLAB parallel computing to detect different products simultaneously. The result shows that this method can detect and identify various defects effectively on the E-TPU midsole’s surface with high detection efficiency. Meanwhile, it can meet the requirements of industrial real-time monitoring performance. However, this method needs a further study for the detection of smaller physical defects.
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References
Bian, Z.G.: Development of machine vision technology. China Instrum. (6), 40–42+65 (2015)
Newman, T.S., Jain, A.K.: A survey of automated visual inspection. Comput. Vis. Image Underst. 61(2), 231–262 (1995)
Li, Y., Gu, P.: Free-form surface inspection techniques state of the art review. Comput. Aided Des. 36(13), 1395–1417 (2004)
Xie, X.: A review of recent advances in surface defect detection using texture analysis techniques. ELCVIA: Electron. Lett. Comput. Vis. Image Anal. 7(3), 1–22 (2008)
Härter, S., Klinger, T., Franke, J., Beer, D.: Comprehensive correlation of inline inspection data for the evaluation of defects in heterogeneous electronic assemblies. In: 2016 Pan Pacific Microelectronics Symposium (Pan Pacific), pp. 1–6. IEEE (2016)
Radovan, S., Papadopoulos, G. D., Georgoudakis, M., Mitropulos, P.: Vision system for finished fabric inspection. In: Machine Vision Applications in Industrial Inspection X, vol. 4664, pp. 97–103. International Society for Optics and Photonics (2002)
Ghazvini, M., Monadjemi, S.A., Movahhedinia, N., Jamshidi, K.: Defect detection of tiles using 2D-wavelet transform and statistical features. World Acad. Sci. Eng. Technol. 49(901–904), 1 (2009)
Deng, S., Latifi, S., Regentova, E.: Document segmentation using polynomial spline wavelets. Pattern Recogn. 34(12), 2533–2545 (2001)
Singhka, D.K.H., Neogi, N., Mohanta, D.: Surface defect classification of steel strip based on machine vision. In: International Conference on Computing and Communication Technologies, pp. 1–5. IEEE (2014)
Shan, T.K., Ma, W.L., Qin, L., Yang, T.: Research on the foaming mechanism and properties of thermoplastic polyurethane elastomer foam materials prepared from supercritical carbon dioxide. China Rubber Ind. 65(05), 514–517 (2018)
Wu, J.W., Yan, J.Q., Fang, Z.H., Xia, Y.: Surface defect detection of slab based on the improved adaboost algorithm. J. Iron Steel Res. (9), 14 (2012)
Świłło, S.J., Perzyk, M.: Automatic inspection of surface defects in die castings after machining. Arch. Foundry Eng. 11 (2011)
Świłło, S.J., Perzyk, M.: Surface casting defects inspection using vision system and neural network techniques. Arch. Foundry Eng. 13(4), 103–106 (2013)
Wong, B.K., Elliott, M.P., Rapley, C.W.: Automatic casting surface defect recognition and classification (1995)
Lu, C.J., Tsai, D.M.: Automatic defect inspection for LCDs using singular value decomposition. Int. J. Adv. Manuf. Technol. 25(1–2), 53–61 (2005)
Zhao, Y.F., Gao, C., Wang, J.G.: Research on surface defect detection algorithm for industrial products based on machine vision. Comput. Appl. Softw. 29(02), 152–154 (2012)
Song, L.M., Li, Z.Y., Chang, Y.L., Xing, G.X., Wang, P.Q., Xi, J.T.: A color phase shift profilometry for the fabric defect detection. Optoelectron. Lett. 10(4), 308–312 (2014)
Acknowledgements
2019.03.13. This work was supported in part by the Natural Science Foundation of China under Grant (61561019), the outstanding young scientific and technological innovation team of Hubei Provincial Department of Education (T201611).
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Li, R., Liu, S., Tang, L., Chen, S., Qin, L. (2020). Surface Defect Detection Method for the E-TPU Midsole Based on Machine Vision. In: Kountchev, R., Patnaik, S., Shi, J., Favorskaya, M. (eds) Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology. Smart Innovation, Systems and Technologies, vol 180. Springer, Singapore. https://doi.org/10.1007/978-981-15-3867-4_17
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