Skip to main content

A Novel RGB-T Based Real-Time Road Detection on Low Cost Embedded Devices

  • Conference paper
  • First Online:
Embedded Systems Technology (ESTC 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 857))

Included in the following conference series:

Abstract

Road detection plays an important role in autonomous driving and driving assistant system. However, the performance of existing methods still suffers from illumination change or bad illumination. In this paper, a novel method is presented which fuses RGB and thermal features to solve these problems. Our method is accurate as well as light-weighted. Evaluating on our RGB-T dataset, the method can achieve 92.01% accuracy and real-time performances on low cost embedded devices.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Notes

  1. 1.

    http://www.engineeringtoolbox.com/specific-heat-capacity-d_391.html.

References

  1. Alvarez, J.M.Á., Lopez, A.M.: Road detection based on illuminant invariance. IEEE Trans. Intell. Transp. Syst. 12(1), 184–193 (2011)

    Article  Google Scholar 

  2. Bar Hillel, A., Lerner, R., Levi, D., Raz, G.: Recent progress in road and lane detection: a survey. Mach. Vis. Appl. 25, 727–745 (2014)

    Article  Google Scholar 

  3. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., Zhang, J., et al.: End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016)

  4. Bojarski, M., Yeres, P., Choromanska, A., Choromanski, K., Firner, B., Jackel, L., Muller, U.: Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv preprint arXiv:1704.07911 (2017)

  5. Caltagirone, L., Scheidegger, S., Svensson, L., Wahde, M.: Fast LiDAR-based road detection using convolutional neural networks. arXiv preprint arXiv:1703.03613 (2017)

  6. Chen, C., Seff, A., Kornhauser, A., Xiao, J.: DeepDriving: learning affordance for direct perception in autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2722–2730 (2015)

    Google Scholar 

  7. Chen, X., Kundu, K., Zhu, Y., Berneshawi, A.G., Ma, H., Fidler, S., Urtasun, R.: 3D object proposals for accurate object class detection. In: Advances in Neural Information Processing Systems, pp. 424–432 (2015)

    Google Scholar 

  8. Fritsch, J., Kuhnl, T., Geiger, A.: A new performance measure and evaluation benchmark for road detection algorithms. In: 2013 16th International IEEE Conference on Intelligent Transportation Systems-(ITSC), pp. 1693–1700. IEEE (2013)

    Google Scholar 

  9. Kühnl, T., Kummert, F., Fritsch, J.: Spatial ray features for real-time ego-lane extraction. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 288–293. IEEE (2012)

    Google Scholar 

  10. Li, C., Wang, G., Ma, Y., Zheng, A., Luo, B., Tang, J.: A unified RGB-T saliency detection benchmark: dataset, baselines, analysis and a novel approach. arXiv preprint arXiv:1701.02829 (2017)

  11. Teichmann, M., Weber, M., Zoellner, M., Cipolla, R., Urtasun, R.: MultiNet: real-time joint semantic reasoning for autonomous driving. arXiv preprint arXiv:1612.07695 (2016)

  12. Xiao, L., Wang, R., Dai, B., Fang, Y., Liu, D., Wu, T.: Hybrid conditional random field based camera-LiDAR fusion for road detection. Inf. Sci. 432, 543–558 (2017)

    Article  MathSciNet  Google Scholar 

  13. Ying, Z., Li, G., Zang, X., Wang, R., Wang, W.: A novel shadow-free feature extractor for real-time road detection. In: Proceedings of the 2016 ACM on Multimedia Conference, pp. 611–615. ACM (2016)

    Google Scholar 

Download references

Acknowledgments

This work was supported by Zhuhai Specially Appointed Scholar Program (No. 67000-42070001).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kai Huang .

Editor information

Editors and Affiliations

Appendices

Appendix

Thermal Images In a Day

Fig. 7.
figure 7

Thermal images in rainbow color palette and corresponding RGB images from 5:30 to 19:30. (Color figure online)

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, W., Cai, X., Huang, K., Zhang, Z. (2018). A Novel RGB-T Based Real-Time Road Detection on Low Cost Embedded Devices. In: Bi, Y., Chen, G., Deng, Q., Wang, Y. (eds) Embedded Systems Technology. ESTC 2017. Communications in Computer and Information Science, vol 857. Springer, Singapore. https://doi.org/10.1007/978-981-13-1026-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1026-3_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1025-6

  • Online ISBN: 978-981-13-1026-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics