Skip to main content

Advertisement

Springer Nature Link
Log in

Improved VGG model-based efficient traffic sign recognition for safe driving in 5G scenarios

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

The rapid development and application of AI in intelligent transportation systems has widely impacted daily life. The application of an intelligent visual aid for traffic sign information recognition can provide assistance and even control vehicles to ensure safe driving. The field of autonomous driving is booming, and great progress has been made. Many traffic sign recognition algorithms based on convolutional neural networks (CNNs) have been proposed because of the fast execution and high recognition rate of CNNs. However, this work addresses a challenging question in the autonomous driving field: how can traffic signs be recognized in real time and accurately? The proposed method designs an improved VGG convolutional neural network and has significantly superior performance compared with existing schemes. First, some redundant convolutional layers are removed efficiently from the VGG-16 network, and the number of parameters is greatly reduced to further optimize the overall architecture and accelerate calculation. Furthermore, the BN (batch normalization) layer and GAP (global average pooling) layer are added to the network to improve the accuracy without increasing the number of parameters. The proposed method needs only 1.15 M when using the improved VGG-16 network. Finally, extensive experiments on the German Traffic Sign Recognition Benchmark (GTSRB) Dataset are performed to evaluate our proposed scheme. Compared with traditional methods, our scheme significantly improves recognition accuracy while maintaining good real-time performance.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Yu J, Li J, Yu Z, Huang Q (2019) Multimodal transformer with multi-view visual representation for image captioning. IEEE Trans Circ Syst Video Technol. https://doi.org/10.1109/TCSVT.2019.2947482

    Article  Google Scholar 

  2. Gao H, Liu C, Li Y, Yang X (2020) V2VR: reliable hybrid-network-oriented V2V data transmission and routing considering rsus and connectivity probability. IEEE Trans Intell Transport Syst. https://doi.org/10.1109/TITS.2020.2983835

    Article  Google Scholar 

  3. Zhang Z, Tan T, Huang K, Wang Y (2012) Practical camera calibration from moving objects for traffic scene surveillance. IEEE Trans Circ Syst Video Technol 23(3):518–533. https://doi.org/10.1109/TCSVT.2012.2210670

    Article  Google Scholar 

  4. Zhang Zhaoxiang, Huang Kaiqi, Wang Yunhong, Li Min (2013) View independent object classification by exploring scene consistency information for traffic scene surveillance. Neurocomputing 99:250–260. https://doi.org/10.1016/j.neucom.2012.07.008

    Article  Google Scholar 

  5. Yao C, Bai X, Liu W et al (2014) Human detection using learned part alphabet and pose dictionary. In: European conference on computer vision. Springer, Cham, pp 251–266

  6. Yu J, Tan M, Zhang H, Tao D, Rui Y (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2019.2932058

    Article  Google Scholar 

  7. Hu C, Bai X, Qi L, Wang X, Xue G, Mei L (2015) Learning discriminative pattern for real-time car brand recognition. IEEE Trans Intell Transport Syst 16(6):3170–3181. https://doi.org/10.1109/TITS.2015.2441051

    Article  Google Scholar 

  8. Xia Yingjie, Weiwei Xu, Zhang Luming, Shi Xingmin, Mao Kuang (2015) Integrating 3d structure into traffic scene understanding with rgb-d data. Neurocomputing 151:700–709. https://doi.org/10.1016/j.neucom.2014.05.091

    Article  Google Scholar 

  9. Honghao G, Yueshen X, Yin Yuyu et al (2019) Context-aware QoS prediction with neural collaborative filtering for Internet-of-Things services. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2019.2956827

    Article  Google Scholar 

  10. Ling Q, Yan J, Li F, Zhang Y (2014) A background modeling and foreground segmentation approach based on the feedback of moving objects in traffic surveillance systems. Neurocomputing 133:32–45. https://doi.org/10.1016/j.neucom.2013.11.034

    Article  Google Scholar 

  11. Stallkamp J, Schlipsing M, Salmen J, Igel C (2011) The German traffic sign recognition benchmark: a multi-class classification competition. In: The international joint conference on neural networks. IEEE, pp 1453–1460. https://doi.org/10.1109/IJCNN.2011.6033395

  12. Mathias M, Timofte R, Benenson R, Van Gool L (2013) Traffic sign recognition—how far are we from the solution?. In: The international joint conference on neural networks (IJCNN). IEEE, pp 1–8. https://doi.org/10.1109/IJCNN.2013.6707049

  13. Rachmadi R, Uchimura K, Koutagi G, Komokata Y (2016) Japan road sign classification using cascade convolutional neural network. In: ITS (Intelligent Transport System) World Congress, Tokyo, pp 1-12. https://doi.org/10.13140/RG.2.2.25436.18566

  14. Zhu Z, Liang D, Zhang S, Huang X, Li B, Hu S (2016) Traffic-sign detection and classification in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2110–2118

  15. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  16. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  17. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. European conference on computer vision. Springer, Cham, pp 818–833

    Google Scholar 

  18. Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  19. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167

  20. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818–2826

  21. Szegedy C, Ioffe S, Vanhoucke, V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence. arXiv:1602.07261

  22. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  23. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141

  24. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708

  25. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520

  26. Ma N, Zhang X, Zheng HT, Sun J (2018) Shufflenet v2: practical guidelines for efficient cnn architecture design. In: Proceedings of the European conference on computer vision (ECCV), pp 116–131

  27. CireşAn D, Meier U, Masci J, Schmidhuber J (2012) Multi-column deep neural network for traffic sign classification. Neural Netw 32:333–338. https://doi.org/10.1016/j.neunet.2012.02.023

    Article  Google Scholar 

  28. Jin J, Fu K, Zhang C (2014) Traffic sign recognition with hinge loss trained convolutional neural networks. IEEE Trans Intell Transport Syst 15(5):1991–2000. https://doi.org/10.1109/TITS.2014.2308281

    Article  Google Scholar 

  29. Cireşan D, Meier U, Masci J, Schmidhuber J (2011) A committee of neural networks for traffic sign classification. In: The international joint conference on neural networks. IEEE, pp 1918–1921. https://doi.org/10.1109/IJCNN.2011.6033458

  30. Wong A, Shafiee MJ, Jules MS (2018) MicronNet: a highly compact deep convolutional neural network architecture for real-time embedded traffic sign classification. IEEE Access 6:59803–59810. https://doi.org/10.1109/ACCESS.2018.2873948

    Article  Google Scholar 

  31. Gao H, Huang W, Duan Y (2020) The cloud-edge based dynamic reconfiguration to service workflow for mobile ecommerce environments: a QoS prediction perspective. ACM Trans Internet Technol. https://doi.org/10.1145/3391198

    Article  Google Scholar 

  32. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Systems Technol (TIST) 2(3):1–27. https://doi.org/10.1145/1961189.1961199

    Article  Google Scholar 

  33. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  34. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 1. IEEE, pp 886–893. https://doi.org/10.1109/CVPR.2005.177

  35. Duda RO, Hart PE (1972) Use of the Hough transformation to detect lines and curves in pictures. Commun ACM 15(1):11–15. https://doi.org/10.1145/361237.361242

    Article  MATH  Google Scholar 

  36. Russakovsky O, Deng J, Su H et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252. https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  37. Stallkamp J, Schlipsing M, Salmen J, Igel C (2012) Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw 32:323–332. https://doi.org/10.1016/j.neunet.2012.02.016

    Article  Google Scholar 

  38. Lin M, Chen Q, Yan S (2013) Network in network. arXiv:1312.4400

  39. Bell S, Lawrence Zitnick C, Bala K, Girshick R (2016) Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2874–2883

  40. Liu W, Rabinovich A, Berg A C (2015) Parsenet: looking wider to see better. arXiv:1506.04579

  41. Li J, Wei Y, Liang X et al (2016) Attentive contexts for object detection. IEEE Trans Multimed 19(5):944–954. https://doi.org/10.1109/TMM.2016.2642789

    Article  Google Scholar 

  42. Li X, Chen S, Hu X, Yang J (2019) Understanding the disharmony between dropout and batch normalization by variance shift. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2682-2690

  43. Martín A, Ashish A, Paul B et al (2015) TensorFlow: large-scale machine learning on heterogeneous systems. https://www.tensorflow.org

  44. Chollet F et al (2015) Keras. https://github.com/keras-team/keras

  45. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980

  46. Luo L, Xiong Y, Liu Y, Sun X (2019) Adaptive gradient methods with dynamic bound of learning rate. arXiv:1902.09843

Download references

Acknowledgements

This work is supported by the National Nature Science Foundation of China (No. 61972357, No. 61672337).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, 200090, People’s Republic of China

    Zhongqin Bi, Ling Yu, Ping Zhou & Hongyang Yao

  2. School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, People’s Republic of China

    Honghao Gao

  3. Gachon University, Gyeonggi-Do, 461-701, South Korea

    Honghao Gao

Authors
  1. Zhongqin Bi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ling Yu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Honghao Gao
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ping Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Hongyang Yao
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Honghao Gao.

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

Bi, Z., Yu, L., Gao, H. et al. Improved VGG model-based efficient traffic sign recognition for safe driving in 5G scenarios. Int. J. Mach. Learn. & Cyber. 12, 3069–3080 (2021). https://doi.org/10.1007/s13042-020-01185-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-020-01185-5

Keywords