Abstract
Impurity detection on tire X-ray image is an indispensable phase in tire quality control and the widely adopted manual inspection could not attain satisfactory performance. In this work we propose an idMKL method to automatically detect impurities by leveraging multiple kernel learning (MKL). idMKL first applies image processing techniques to separate different regions of a tire image and suppress their normal texture characteristics. As a result, candidate blobs containing both true impurities and false alarms are obtained. We extract different features from the blobs and evaluate their effectiveness in impurity detection. MKL is then employed to adaptively combine the features to maximize the detection performance. Experiments on thousands of images show that idMKL can well separate the blobs and achieves promising results in tire impurity detection. Moreover, idMKL has been adopted as a mean complementary to the manual inspection by tire factories and shown to be effective.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guo, Q., Wei, Z.: Tire defect detection using image component decomposition. Res. J. Appl. Sci. Eng. Technol. 4(1), 41–44 (2012)
Zhang, Y., Li, T., Li, Q.: Defect detection for tire laser shearography image using curvelet transform based edge detector. Opt. Laser Technol. 47(4), 64–71 (2013)
Zhang, Y.: Research on nondestructive tire defect detection using computer vision methods. Ph.D. thesis, Qingdao University of Science and Technology (2014). (in Chinese)
Zhang, Y., Dimitr, L., Li, Q.L.: Automatic detection of defects in tire radiographic image. IEEE Trans. Autom. Sci. Eng. 1–9 (2017, accepted)
Xiang, Y., Zhang, C., Guo, Q.: A dictionary-based method for tire defect detection. In: IEEE International Conference on Information and Automation, pp. 519–523 (2014)
Tajeripour, F., Kabir, E., Sheikhi, A.: Fabric defect detection using modified local binary patterns. EURASIP J. Adv. Sign. Process. 2008(1), 1–12 (2007)
Kumar, A.: Computer-vision-based fabric defect detection: a survey. IEEE Trans. Indus. Electron. 55(1), 348–363 (2008)
Cui, X., Liu, Y., Wang, C., Li, H.: A novel method for feature extraction and automatic recognition of tire defects. In: ICIMM International Conference on Intelligent Manufacturing and Materials, pp. 1–9 (2016)
Qiu, S., Lane, T.: A framework for multiple kernel support vector regression and its applications to sIRNA efficacy prediction. IEEE/ACM Trans. Comput. Biol. Bioinform. 6(2), 190–199 (2009)
Kumar, A., Sminchisescu, C.: Support kernel machines for object recognition. In: IEEE International Conference on Computer Vision, pp. 1–8 (2007)
Ke, Y., Youbin, C., David, Z.: Gabor surface feature for face recognition. In: Pattern Recognition, pp. 288–292 (2011)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)
Crosier, M., Griffin, L.D.: Using basic image features for texture classification. Int. J. Comput. Vis. 88(3), 447–460 (2010)
Varma, M., Ray, D.: Learning the discriminative power-invariance trade-off. In: IEEE International Conference on Computer Vision, pp. 1–8 (2007)
Gao, X., Gu, Z., et al.: ContainerLeaks: emerging security threats of information leakages in container clouds. In: IEEE International Conference on Dependable Systems and Networks, pp. 1–12 (2017)
Gao, X., Liu D., et al.: E-Android: a new energy profiling tool for smartphones. In: 37th IEEE International Conference on Distributed Computing Systems, pp. 492–502 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Zhao, S., Chen, Z., Li, B., Zhang, B. (2018). Tire X-ray Image Impurity Detection Based on Multiple Kernel Learning. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-77380-3_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77379-7
Online ISBN: 978-3-319-77380-3
eBook Packages: Computer ScienceComputer Science (R0)