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Estimation of Tyre Pressure from the Characteristics of the Wheel: An Image Processing Approach

  • V. B. Vineeth ReddyEmail author
  • H. Ananda Rao
  • A. Yeshwanth
  • Pravin Bhaskar Ramteke
  • Shashidhar G. Koolagudi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 766)

Abstract

Improper tyre pressure is a safety issue that falls prey to ignorance of users. But a drop in tyre pressure can result in the reduction of mileage, tyre life, vehicle safety and performance. In this paper, an approach is proposed to measure the tyre pressure from the image of the wheel. The tyre pressure is classified into under pressure and normal pressure using load index, tyre type, tyre position and ratio of compressed and uncompressed tyre radius. The efficiency of the feature is evaluated using three classifiers namely Random Forest, AdaBoost and Artificial Neural Networks. It is observed that the ratio of radii plays a major role in classifying the tyres. The proposed system can be used to obtain a rough idea on whether the tyre should be refilled or not.

Keywords

Image processing Random forest AdaBoost Hough gradient Neural networks 

References

  1. 1.
    Mathai, A., Vanaja Ranjan, P.: A new approach to tyre pressure monitoring system. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. (2015)Google Scholar
  2. 2.
    Mule, S., Ingle, K.S.: Review of wireless tyre pressure monitoring system for vehicle using wireless communication. Int. J. Innov. Res. Comput. Commun. Eng. (2017)Google Scholar
  3. 3.
    Minca, C.: The determination and analysis of tire contact surface geometric parameters. Review of the Air Force Academy (2015)Google Scholar
  4. 4.
    Shetty, P.: Circle detection in images. Ph.D. thesis, San Diego State University, Department of Electrical Engineering (2011)Google Scholar
  5. 5.
    Blaser, R.: Piotr Fryzlewicz random rotation ensembles. J. Mach. Learn. Res. (2015)Google Scholar
  6. 6.
    Wang, R.: AdaBoost for feature selection, classification and its relation with SVM*, a review. In: International Conference on Solid State Devices and Materials Science (2012)Google Scholar
  7. 7.
    Philipp, G., Carbonell, J.G.: Non-parametric neural networks. In: ICLR (2017)Google Scholar
  8. 8.
    Tu, J.V.: Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. (1996)Google Scholar
  9. 9.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. (2011)Google Scholar
  10. 10.
    Vanwinckelen, G., Blockeel, H.: On estimating model accuracy with repeated cross-validation. In: BeneLearn and PMLS (2012)Google Scholar
  11. 11.
    Li, X., Wang, L., Sung, E.: Improving AdaBoost for classification on small training sample sets with active learningGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • V. B. Vineeth Reddy
    • 1
    Email author
  • H. Ananda Rao
    • 1
  • A. Yeshwanth
    • 1
  • Pravin Bhaskar Ramteke
    • 1
  • Shashidhar G. Koolagudi
    • 1
  1. 1.National Institute of Technology KarnatakaSurathkalIndia

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