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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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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