Abstract
Radial tires have a large market share in the tire market due to their better wear resistance and puncture resistance. However, the complexity of the production process makes impurities and irregular cord spacing defects often appear in the body cord area. In this paper, a method relying on X-ray images to detect impurities and irregular cord spacing defects is proposed. The detection problems of these two types of defects are transformed into calculating cord pixel spacing and background connected domains. Firstly, the tire crown, tire ring, and tire body regions are segmented by a novel semantic segmentation network (SSN). Then the background and the cord are separated by an adaptive binarization method. Finally, the irregular cord spacing defects are detected through the refinement and column statistics. The impurities of tire body are located by the marks of the connected domains. The experimental results of the X-ray images show that this method can meet the positioning requirements of irregular shaped impurities. A idea of setting the threshold column effectively improves the detection speed of irregular cord spacing. In addition, the detection accuracy rates for both types of defects are higher than 90\(\%\), which is helpful for further research on various types of tire defects and the design of an automatic tire defect identification system.
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References
Zhou, Y.: Design of manufacturing execution system in tire enterprises. Trans. Tech. Publ. Ltd. 411, 2343–2346 (2013)
Zhang, Y., Cui, X., Liu, Y., Yu, B.: Tire defects classification using convolution architecture for fast feature embedding. Int. J. Comput. Intell. Syst. 11(1), 1056–1066 (2018)
Zhao, G., Qin, S.: High-precision detection of defects of tire texture through X-ray imaging based on local inverse difference moment features. Sensors 18(8), 2524 (2018)
Guo, Q., Zhang, C., Liu, H., Zhang, X.: Defect detection in tire X-ray images using weighted texture dissimilarity. J. Sens. (2016)
Zhang, Y., Lefebvre, D., Li, Q.: Automatic detection of defects in tire radiographic images. IEEE Trans. Autom. Sci. Eng. 14(3), 1378–1386 (2015)
Zhang, Y., Li, T., Li, Q.: Defect detection for tire laser shearography image using curvelet transform based edge detector. Opt. Laser Technol. 47, 64–71 (2013)
Zhang, Y., Li, T., Li, Q.: Detection of foreign bodies and bubble defects in tire radiography images based on total variation and edge detection. Chin. Phys. Lett. 30(8), 084205 (2013)
Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends Sig. Process. 7(3), 197–387 (2014)
Chen, J., Li, Y., Zhao, J.: X-ray of tire defects detection via modified faster R-CNN. In: 2019 2nd International Conference on Safety Produce Informatization (IICSPI), pp. 257–260 (2019)
Cui, X., Liu, Y., Zhang, Y., Wang, C.: Tire defects classification with multi-contrast convolutional neural networks. Int. J. Pattern Recogn. Artif. Intell. 32(4), 1850011 (2018)
Zheng, G., et al.: Development of a gray-level co-occurrence matrix-based texture orientation estimation method and its application in sea surface wind direction retrieval from SAR imagery. IEEE Trans. Geosci. Remote Sens. 56(9), 5244–5260 (2018)
Jacob, N., Kordi, B., Sherif, S.: Assessment of power transformer paper ageing using wavelet texture analysis of microscopy images. IEEE Trans. Dielectr. Electr. Insul. 27(6), 1898–1905 (2020)
Lin, C.M., Tsai, C.Y., Lai, Y.C., Li, S.A., Wong, C.: Visual object recognition and pose estimation based on a deep semantic segmentation network. IEEE Sens. J. 18(22), 9370–9381 (2018)
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Yi, X., Peng, C., Yang, M., Masroor, S. (2021). Tire Body Defect Detection: From the Perspective of Industrial Applications. In: Han, Q., McLoone, S., Peng, C., Zhang, B. (eds) Intelligent Equipment, Robots, and Vehicles. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1469. Springer, Singapore. https://doi.org/10.1007/978-981-16-7213-2_72
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DOI: https://doi.org/10.1007/978-981-16-7213-2_72
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