Shape Recognition of Tyre Marking Points Based on Support Vector Machine

ABSTRACT

With the rapid development of automobile industry, the demand of tyre is increasing greatly. But tyre classification implementation is still not efficient and effective for tyre industry. This paper proposes a method of tyre classification using tyre marking points indicating shapes based on support vector machine (SVM). First, the tyre marking points printed on the profiles of tyre are acquired by a designed image acquisition device. Second, those tyre marking points images are handled by image preprocessing methods of image denoising, segmentation, and edge detection. After image preprocessing, the contour coordinates of tyre marking points are extracted. The Fourier transform method is further applied to the extracted contour coordinates to acquire the Fourier descriptors of tyre marking points as feature vectors. Followed, those extracted Fourier descriptors are inputted as SVM classifiers. Finally, the proposed classification techniques based on SVM is employed to classify tyre as corresponding types according to shape recognition of tyre marking points. The experimental results show that the proposed tyre classification method can meet the requirement of tyre industry by a mean shape recognition accuracy of 97.25%.

Keywords:

• SVM
• Shape recognition
• Tyre marking points
• Tyre industry

ACKNOWLEDGEMENTS

The authors deeply thank the reviewers for their useful comments and suggestions, and they would like to thank the editors of Taylor & Francis for their help in proofreading the paper.

Additional information

Notes on contributors

Yong Wang

Yong Wang is currently a master's candidate at School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China. His research interests include machine vision and mechanical design and theory.

E-mail: [email protected].

Hui Guo

Hui Guo is currently an associate professor at School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China. Her research interests include computer graphics and reverse engineering.

E-mail: [email protected]