Skip to main content
SpringerLink
Log in

A modified faster region-based convolutional neural network approach for improved vehicle detection performance

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Presently available algorithms employed for vehicle detection exhibit three main disadvantages: slow detection speed, poor small objects detection, and low detection precision. To solve above problems, the present work proposes a vehicle detection approach employing a modified faster region-based convolutional neural network (R-CNN). Firstly, this approach introduces a deep CNN-based on VGG-16 and inception architecture, and adds a set of convolutional kernels with a 1 × 1 size, called a deep convolutional network (DCN). Then, an accurate vehicle region network (AVRN) and a vehicle attribute learning network (VALN) are designed. The AVRN accurately generates vehicle-like regions in real time, and the VALN detects the corresponding classifications and locations of vehicle-like regions. To improve the detection precision, we introduce corresponding loss functions for the AVRN and VALN. The calculation speed is increased by alternately optimizing and jointly training the AVRN and VALN. Experimental results demonstrate that the modified faster R-CNN approach improves significantly vehicle detection performance relative to existing algorithms, where, compared to the standard state-of-the-art faster R-CNN vehicle detection approach, the mean average precision of the test results obtained by the modified approach is increased by 11% and the detection time is reduced by one-third.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Adankon MM, Cheriet M (2002) Support vector machine. Comput Sci 1 (4):1–28. https://doi.org/10.1007/978-0-387-73003-5-299

    MATH  Google Scholar 

  2. Bouchard G (2011) Clustering and classification employing softmax function including efficient bounds

  3. Chen Z, Wang C, Wen C et al (2015) Vehicle detection in high-resolution aerial images via sparse representation and superpixels. IEEE Trans Geosci Remote Sens 54(1):103–116

    Article  Google Scholar 

  4. Cheng G (2016) A survey on object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens 117:11–28. https://doi.org/10.1016/j.isprsjprs.2016.03.014

    Article  Google Scholar 

  5. Cheng G, Zhou P, Han J (2016) Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images. IEEE Trans Geosci Remote Sens 54(12):7405–7415

    Article  Google Scholar 

  6. Cheng HY, Weng CC, Chen YY (2012) Vehicle detection in aerial surveillance using dynamic bayesian networks. IEEE Trans Image Process 21(4):2152–2159

    Article  MathSciNet  MATH  Google Scholar 

  7. Dollr P, Appel R, Belongie S et al (2014) Fast feature pyramids for object detection. IEEE Trans Pattern Anal Mach Intell 36(8):1532–1545. https://doi.org/10.1109/TPAMI.2014.2300479

    Article  Google Scholar 

  8. Fan Q, Brown L, Smith J (2016) A closer look at faster r-cnn for vehicle detection. Intelligent Vehicles Symposium, pp 124–129

  9. Girshick R (2015) Fast r-CNN. In: IEEE international conference on computer vision, pp 1440–1448, Santiago, Chile

  10. Girshick R, Donahue J, Darrell T et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE conference on computer vision and pattern recognition, pp 580–587. Columbus, OH, USA

  11. Girshick R, Donahue J, Darrell T, et al. (2016) Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142–158

    Article  Google Scholar 

  12. He K, Zhang X, Ren S et al (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37 (9):1904–1916. https://doi.org/10.1109/TPAMI.2015.2389824

    Article  Google Scholar 

  13. He K, Zhang X, Ren S et al (2016) Deep residual learning for image recognition. In: Computer vision and pattern recognition, pp 770–778. Las Vegas, NV, USA

  14. Jazayeri A, Cai H, Zheng JY et al (2011) Vehicle detection and tracking in car video based on motion model. IEEE Trans Intell Transp Syst 12(2):583–595

    Article  Google Scholar 

  15. Kong T, Yao A, Chen Y et al (2016) Hypernet: Towards accurate region proposal generation and joint object detection. IEEE Conf Comput Vis Pattern Recognit, pp 845–853

  16. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Int Conf Neural Inf Process Syst 1:1097–1105

    Google Scholar 

  17. Leitloff J, Rosenbaum D, Kurz F et al (2014) An operational system for estimating road traffic information from aerial images. Remote Sens 6(11):11315–11341

    Article  Google Scholar 

  18. Liu W, Anguelov D, Erhan D et al (2016) Ssd: Single shot multibox detector. Eur Conf Comput Vis 9905:21–37

    Article  Google Scholar 

  19. Liu K, Mattyus G (2015) Fast multiclass vehicle detection on aerial images. IEEE Geosci Remote Sens Lett 12(9):1938–1942. https://doi.org/10.1109/LGRS.2015.2439517

    Article  Google Scholar 

  20. Lu Y, Chowdhery A, Kandula S (2016) Optasia: a relational platform for efficient large-scale video analytics. ACM Symposium on Cloud Computing, pp 57–70

  21. Noh SJ, Shim D, Jeon M (2016) Adaptive sliding-window strategy for vehicle detection in highway environments. IEEE Trans Intell Transp Syst 17(2):323–335

    Article  Google Scholar 

  22. Redmon J, Divvala S, Girshick R et al (2015) You only look once: unified, real-time object detection. IEEE Conf Comput Vis Pattern Recognit, pp 779–788

  23. Ren S, He K, Girshick R et al (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  24. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Comput Sci, pp 1–13

  25. Sun Z, Bebis G, Miller R (2002) On-road vehicle detection using gabor filters and support vector machines. Int Conf Digital Signal Process 2:1019–1022

    Article  Google Scholar 

  26. Sun Z, Bebis G, Miller R (2005) On-road vehicle detection using evolutionary gabor filter optimization. IEEE Trans Intell Transp Syst 6(2):125–137

    Article  Google Scholar 

  27. Sun Z, Bebis G, Miller R (2006) On-road vehicle detection: a review. IEEE Trans Pattern Anal Mach Intell 28(5):694–711

    Article  Google Scholar 

  28. Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions. In: IEEE Conference on computer vision and pattern recognition, pp 1–9. Boston, MA, USA

  29. Uijlings JR, Sande KE, Gevers T et al (2013) Selective search for object recognition. Int J Comput Vis 104(2):154–171. https://doi.org/10.1007/s11263-013-0620-5

    Article  Google Scholar 

  30. Wang G, Wang X, Fan B et al (2017) Feature extraction by rotation-invariant matrix representation for object detection in aerial image. IEEE Geosci Remote Sens Lett PP(99):1–5

    Google Scholar 

  31. Zhang X, Zou J, Ming X et al (2015) Efficient and accurate approximations of nonlinear convolutional networks. In: IEEE conference on computer vision and pattern recognition, pp 1984–1992. Boston, MA, USA

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. U1404617), Outstanding Youth Project of Science and Technology Innovation Talent Program of Henan Province (No.174100510011), and Program for Innovative Research Team (in Science and Technology) in Henan Province University (No.16IRTSTHN026).

Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Henan University of Technology, Lianhua Avenue 100, Zhengzhou, 450001, China

    Qinghui Zhang, Chenxia Wan & Weiliang Han

Authors
  1. Qinghui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chenxia Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weiliang Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qinghui Zhang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Q., Wan, C. & Han, W. A modified faster region-based convolutional neural network approach for improved vehicle detection performance. Multimed Tools Appl 78, 29431–29446 (2019). https://doi.org/10.1007/s11042-018-6769-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6769-8

Keywords

Search

Navigation