Skip to main content

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

  • Conference paper
  • First Online:
Smart Computing Paradigms: New Progresses and Challenges

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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. 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. Minca, C.: The determination and analysis of tire contact surface geometric parameters. Review of the Air Force Academy (2015)

    Google Scholar 

  4. Shetty, P.: Circle detection in images. Ph.D. thesis, San Diego State University, Department of Electrical Engineering (2011)

    Google Scholar 

  5. Blaser, R.: Piotr Fryzlewicz random rotation ensembles. J. Mach. Learn. Res. (2015)

    Google Scholar 

  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. Philipp, G., Carbonell, J.G.: Non-parametric neural networks. In: ICLR (2017)

    Google Scholar 

  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. 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. Vanwinckelen, G., Blockeel, H.: On estimating model accuracy with repeated cross-validation. In: BeneLearn and PMLS (2012)

    Google Scholar 

  11. Li, X., Wang, L., Sung, E.: Improving AdaBoost for classification on small training sample sets with active learning

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to V. B. Vineeth Reddy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vineeth Reddy, V.B., Ananda Rao, H., Yeshwanth, A., Ramteke, P.B., Koolagudi, S.G. (2020). Estimation of Tyre Pressure from the Characteristics of the Wheel: An Image Processing Approach. In: Elçi, A., Sa, P., Modi, C., Olague, G., Sahoo, M., Bakshi, S. (eds) Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing, vol 766. Springer, Singapore. https://doi.org/10.1007/978-981-13-9683-0_1

Download citation

Publish with us

Policies and ethics