Skip to main content
SpringerLink
Log in

Deep learning-based algorithm for vehicle detection in intelligent transportation systems

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Object detection is an essential technology in the computer vision domain and plays a vital role in intelligent transportation. Intelligent vehicles utilize object detection on images for environment perception. This work develops a target detection algorithm based on deep learning technologies, particularly convolutional neural networks and neural network modeling. Building on the analysis of the traditional Haar-like vehicle recognition algorithm, a vehicle recognition algorithm based on a convolutional neural network with fused edge features (FE-CNN) is proposed. The experimental results demonstrate that FE-CNN improves the recognition precision and the model’s convergence speed through a simple and effective edge feature fusion method. In the experiment conducted using real traffic scene for vehicle recognition, the developed algorithm achieves a 99.82% recognition rate in efficient time, demonstrating the capability for real-time performance and accurate target detection.

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

Similar content being viewed by others

References

  1. Li H, Wang X (2017) Vehicle feature recognition method based on deep neural network. J Henan Inst Eng (Nat Sci Ed) 29(04):44–48

    Google Scholar 

  2. Shi J (2017) Research on vehicle identification based on neural network algorithm. Comput Dig Eng 45(12):2336–2340

    Google Scholar 

  3. Yang W, Gong J, Wei L (2016) Front vehicle image recognition based on multi feature information. J Chang’ an Univ (Nat Sci Ed) 36(04):79–85

    Google Scholar 

  4. Belmiloud D, Benkedjouh T, Lachi M, Laggoun A, Dron JP (2018) Deep convolutional neural networks for Bearings failure prediction and temperature correlation. J Vibroeng 20:1–14. https://doi.org/10.21595/jve.2018.19637

    Article  Google Scholar 

  5. Li N, Zhilei W, Zhou Fu LP (2018) Vehicle recognition technology based on computer vision. Autom Pract Technol 24:39–40

    Google Scholar 

  6. Wang S (2017) Research on vehicle information recognition technology based on neural network learning. Nanjing University of Posts and Telecommunications

  7. Yang Z, Kuang N, Fan L, Kang B (2018) Overview of image classification algorithm based on convolutional neural network. Signal Process 34(12):1474–1489

    Google Scholar 

  8. Geng Q (2016) Research on key technologies of intelligent transportation system based on image recognition theory. Jilin University

  9. Cai W (2018) Research on high speed vehicle detection based on convolutional neural network. Nanjing University of Posts and Telecommunications

  10. Liu X, Sun H, Ma C, Jiang L (2019) Vehicle recognition model based on convolutional neural network multi feature combination. Comput Sci 46(S1):254–258

    Google Scholar 

  11. Boram PARK (2018) Authorship attribution in Huayan texts by machine learning using N-gram and SVM. Int J Budd Thought Cult 28(2):122–126

    Google Scholar 

  12. Ye DC et al (2013) A novel and better fitness evaluation for rough set based minimum; attribute reduction problem. Inf Sci 222(3):413–423

    Article  MathSciNet  Google Scholar 

  13. Xia Y, Leung H (2014) Performance analysis of statistical optimal data fusion algorithms. Inf Sci 277:808–824

    Article  MathSciNet  Google Scholar 

  14. Guo W, Chen G (2015) Human action recognition via multi-task learning base on spatial–temporal feature. Inf Sci 320:418–428

    Article  MathSciNet  Google Scholar 

  15. Guo K, Guo W, Chen Y et al (2015) Community discovery by propagating local and global information based on the MapReduce model. Inf Sci 323:73–93

    Article  MathSciNet  Google Scholar 

  16. Yang LH, Wang YM, Su Q et al (2016) Multi-attribute search framework for optimizing extended belief rule-based systems. Inf Sci 370:159–183

    Article  Google Scholar 

  17. Cheng H, Su Z, Xiong N et al (2016) Energy-efficient node scheduling algorithms for wireless sensor networks using Markov Random Field model. Inf Sci 329:461–477

    Article  Google Scholar 

  18. Wang J, Zhang X M, Lin Y et al (2018) Event-triggered dissipative control for networked stochastic systems under non-uniform sampling. Inf Sci S0020025518301749

  19. Xia Y, Chen T, Shan J (2014) A novel iterative method for computing generalized inverse. Neural Comput 26(2):449–465

    Article  MathSciNet  Google Scholar 

  20. Zhong S, Chen T, He F et al (2014) Fast Gaussian kernel learning for classification tasks based on specially structured global optimization. Neural Netw 57:51–62

    Article  Google Scholar 

  21. Zhang S, Xia Y, Wang J (2015) A complex-valued projection neural network for constrained optimization of real functions in complex variables. IEEE Trans Neural Netw Learn Syst 26(12):3227–3238

    Article  MathSciNet  Google Scholar 

  22. Xia Y, Wang J (2015) Low-dimensional recurrent neural network-based Kalman filter for speech enhancement. Neural Netw 67:131–139

    Article  Google Scholar 

  23. Zhang S, Xia Y, Zheng W (2015) A complex-valued neural dynamical optimization approach and its stability analysis. Neural Netw 61:59–67

    Article  Google Scholar 

  24. Liu G, Huang X, Guo W et al (2015) Multilayer obstacle-avoiding x-architecture steiner minimal tree construction based on particle swarm optimization. IEEE Trans Cybern 45(5):989–1002

    Google Scholar 

  25. Xia Y, Wang J (2016) A bi-projection neural network for solving constrained quadratic optimization problems. IEEE Trans Neural Netw Learn Syst 27(2):214–224

    Article  MathSciNet  Google Scholar 

  26. Zhang S, Xia Y (2016) Two fast complex-valued algorithms for solving complex quadratic programming problems. IEEE Trans Cybern 46(12):2837–2847

    Article  MathSciNet  Google Scholar 

  27. Huang Z, Yu Y, Gu J et al (2017) An efficient method for traffic sign recognition based on extreme learning machine. IEEE Trans Cybern 47(4):920–933

    Article  Google Scholar 

  28. Kun G, Qishan Z (2013) Fast clustering-based anonymization approaches with time constraints for data streams. Knowl Based Syst 46:95–108

    Article  Google Scholar 

  29. Chen X, Jian C (2014) Gene expression data clustering based on graph regularized subspace segmentation. Neurocomputing 143:44–50

    Article  Google Scholar 

  30. Genggeng L, Zhisheng C, Zhen Z, Wenzhong G, Guolong C (2020) A unified algorithm based on HTS and self-adapting PSO for the construction of octagonal and rectilinear SMT. Soft Comput 24(6):3943–3961. https://doi.org/10.1007/s00500-019-04165-2

    Article  Google Scholar 

  31. Genggeng L, Xing H, Wenzhong G, Yuzhen N, Guolong C (2015) Multilayer obstacle-avoiding X-architecture Steiner minimal tree construction based on particle swarm optimization. IEEE Trans Cybern 45(5):989–1002. https://doi.org/10.1109/TCYB.2014.2342713

    Article  Google Scholar 

  32. Genggeng L, Wenzhong G, Yuzhen N, Guolong C, Xing H (2015) A PSO-based-timing-driven Octilinear Steiner Tree Algorithm for VLSI routing considering bend reduction. Soft Comput 19(5):1153–1169. https://doi.org/10.1007/s00500-014-1329-2

    Article  MATH  Google Scholar 

  33. Genggeng L, Wenzhong G, Rongrong L, Yuzhen N, Guolong C (2015) XGRouter: high-quality global router in X-architecture with particle swarm optimization. Front Comput Sci 9(4):576–594

    Article  Google Scholar 

  34. Guo W, Liu G, Chen G, Peng S (2014) A hybrid multi- objective PSO algorithm with local search strategy for VLSI partitioning. Front Comput Sci 8(2):203–216. https://doi.org/10.1007/s11704-014-3008-y

    Article  MathSciNet  Google Scholar 

  35. Xing H, Genggeng L, Wenzhong G, Yuzhen N, Guolong C (2015) Obstacle-avoiding algorithm in X-architecture based on discrete particle swarm optimization for VLSI design. ACM Trans Des Autom Electron Syst 20(2):28. https://doi.org/10.1145/2742143 ((Article 24))

    Article  Google Scholar 

  36. Xing H, Wenzhong G, Genggeng L, Guolong C (2016) FH-OAOS: a fast 4-step heuristic for obstacle-avoiding octilinear architecture router construction. ACM Trans Des Autom Electron Syst 21(3):30. https://doi.org/10.1145/2856033 ((Article 48))

    Article  Google Scholar 

  37. Xing H, Wenzhong G, Genggeng L, Guolong C (2017) MLXR: multi-layer obstacle-avoiding X-architecture Steiner tree construction for VLSI routing. Sci China Inf Sci 60(1):1–3

    Google Scholar 

  38. ImageNet Image Database [DB/OL]. http://www.image-net.org/download

Download references

Acknowledgements

This work is supported by Department of Education of Guangdong Province Project (GKY-2019CQYJ-5) and (GKY-2020KYZDK-7) and GDAS' Project of Science and Technology Development (nos. 2020GDASYL-20200402007 and 2018GDASCX-0115). The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group No. RG-1441-331.

Author information

Authors and Affiliations

  1. Guangdong University of Science and Technology, Dongguan, Guangdong, China

    Linrun Qiu

  2. Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, Guangdong, China

    Dongbo Zhang

  3. School of Computer Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu, China

    Yuan Tian

  4. Department Computer Science, King Saud University, Riyadh, Kingdom of Saudi Arabia

    Najla Al-Nabhan

Authors
  1. Linrun Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dongbo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuan Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Najla Al-Nabhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dongbo 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

Qiu, L., Zhang, D., Tian, Y. et al. Deep learning-based algorithm for vehicle detection in intelligent transportation systems. J Supercomput 77, 11083–11098 (2021). https://doi.org/10.1007/s11227-021-03712-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03712-9

Keywords

Search

Navigation