Skip to main content
Springer Nature Link
Log in

Unveiling Interpretability: Analyzing Transfer Learning in Deep Learning Models for Traffic Sign Recognition

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Since the advent of automobiles and driver assistance technologies, traffic sign recognition has been of the utmost importance for Industry 4.0. In the driving system, good data pre-processing is critical. For such objectives, sophisticated transformations or fundamentally computational image processing approaches are out of the question. Convolutional Neural Networks (CNN) have been used to perform more object identification challenges, thus, improving most computer vision applications, both existing and new, because of their excellent recognition rate and rapid execution. This study introduces a method for recognizing traffic signs by utilizing a CNN-based model and the transfer learning concept. The TensorFlow library is used for training the underlying neural network model. The offered approach makes use of the German Traffic Sign Recognition Benchmark (GTSRB) and images from the Traffic Sign Images from Turkey (TSIT) databases. These datasets are dependable and vibrant, and they have been used to train many algorithms. Furthermore, after training the model, the proposed scheme acquired a testing accuracy is 99.44%.

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

Similar content being viewed by others

Explore related subjects

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

Data Availability

The data that support the findings of this study are publicly available [19, 20].

References

  1. World Health Administration, Global Plan for the Decade of Action for Road Safety 2021–30. 2018. Available: https://www.who.int/publications/m/item/global-plan-for-the-decade-of-action-for-road-safety-2021-2030.

  2. Abid F, Rasheed J, Hamdi M, Alshahrani H, Al Reshan MS, Shaikh A. Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network. Neural Comput Appl. 2024. https://doi.org/10.1007/s00521-024-09771-2.

    Article  Google Scholar 

  3. Albawi S, Mohammed TA, Al-Zawi S. Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET). 2017. pp 1–6.

  4. Waziry S, Wardak AB, Rasheed J, Shubair RM, Rajab K, Shaikh A. Performance comparison of machine learning driven approaches for classification of complex noises in quick response code images. Heliyon. 2023;9(4):e15108. https://doi.org/10.1016/j.heliyon.2023.e15108.

    Article  Google Scholar 

  5. Farooq MS, et al. A conceptual multi-layer framework for the detection of nighttime pedestrian in autonomous vehicles using deep reinforcement learning. Entropy. 2023;25(1):135. https://doi.org/10.3390/e25010135.

    Article  Google Scholar 

  6. Boujemaa KS, Bouhoute A, Boubouh K, Berrada I (2017) Traffic sign recognition using convolutional neural networks. Proceedings—2017 International Conference on Wireless Networks and Mobile Communications, WINCOM 2017; 2017. p. 1–12.

  7. MM Lau, KH Lim, AA Gopalai. Malaysia traffic sign recognition with convolutional neural network. In: International Conference on Digital Signal Processing, DSP, vol. 2015-Septe. 2015. p. 1006–10.

  8. Fuchao W, Bin F, Luo H, Yang Y, Tong B. Traffic sign recognition using a multi-task convolutional neural network. IEEE Trans Intell Transp Syst. 2018;19(4):1100–11. https://doi.org/10.1109/TITS.2017.2714691.

    Article  Google Scholar 

  9. Shustanov A, Yakimov P. CNN design for real-time traffic sign recognition. Procedia Eng. 2017;201:718–25. https://doi.org/10.1016/j.proeng.2017.09.594.

    Article  Google Scholar 

  10. Liu C, Chang F, Chen Z, Liu D. Fast traffic sign recognition via high-contrast region extraction and extended sparse representation. IEEE Trans Intell Transp Syst. 2016;17(1):79–92. https://doi.org/10.1109/TITS.2015.2459594.

    Article  Google Scholar 

  11. Zeng Y, Xu X, Fang Y, Zhao K. Traffic sign recognition using deep convolutional networks and extreme learning machine. In: He X, Gao X, Zhang Y, Zhou ZH, Liu ZY, Fu B, Hu F, Zhang Z, editors. Intelligence Science and Big Data Engineering. Image and Video Data Engineering, Image Proc., vol. 9242. Springer; 2015. p. 272–80.

    Chapter  Google Scholar 

  12. Wei W, et al. A lightweight network for traffic sign recognition based on multi-scale feature and attention mechanism. Heliyon. 2024;10(4):e26182. https://doi.org/10.1016/j.heliyon.2024.e26182.

    Article  Google Scholar 

  13. Hamza A, Nawal S. Traffic sign classification using deep learning comparative study. Procedia Comput Sci. 2024;233:939–49. https://doi.org/10.1016/j.procs.2024.03.283.

    Article  Google Scholar 

  14. Saxena S, Dey S, Shah M, Gupta S. Traffic sign detection in unconstrained environment using improved YOLOv4. Expert Syst Appl. 2024;238:121836. https://doi.org/10.1016/j.eswa.2023.121836.

    Article  Google Scholar 

  15. Youssouf N. Traffic sign classification using CNN and detection using faster-RCNN and YOLOV4. Heliyon. 2022;8(12):e11792. https://doi.org/10.1016/j.heliyon.2022.e11792.

    Article  Google Scholar 

  16. Rani AR, Anusha Y, Cherishama SK, Laxmi SV. Traffic sign detection and recognition using deep learning-based approach with haze removal for autonomous vehicle navigation. E-Prime Adv Electr Eng Electr Energy. 2024;7:100442. https://doi.org/10.1016/j.prime.2024.100442.

    Article  Google Scholar 

  17. Latif G, Alghmgham DA, Maheswar R, Alghazo J, Sibai F, Aly MH. Deep learning in Transportation: Optimized driven deep residual networks for Arabic traffic sign recognition. Alex Eng J. 2023;80:134–43. https://doi.org/10.1016/j.aej.2023.08.047.

    Article  Google Scholar 

  18. Qiao X. Research on traffic sign recognition based on CNN deep learning network. Procedia Comput Sci. 2023;228:826–37. https://doi.org/10.1016/j.procs.2023.11.102.

    Article  Google Scholar 

  19. Für Neuroinformatik I. GTSRB—German Traffic Sign Recognition Benchmark | Kaggle. 2019. https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign. Accessed 05 Jan 2024.

  20. Yucesan E. Traffic Sign Images From Turkey. 2020. https://www.kaggle.com/datasets/erdicem/traffic-sign-images-from-turkey. Accessed 10 Jan 2024.

  21. Lin C, Li L, Luo W, Wang KCP, Guo J. Transfer learning based traffic sign recognition using inception-v3 model. Period Polytech Transp Eng. 2019;47(3):242–50. https://doi.org/10.3311/PPtr.11480.

    Article  Google Scholar 

  22. Filus K, Domańska J. Software vulnerabilities in TensorFlow-based deep learning applications. Comput Secur. 2023;124:102948. https://doi.org/10.1016/j.cose.2022.102948.

    Article  Google Scholar 

  23. Huang SC, Le TH. Environment installation. In: Principles and Labs for Deep Learning. Elsevier; 2021. p. ix–xxv.

    Chapter  Google Scholar 

  24. Haagsman E. Collaboration with Anaconda, Inc. PyCharm Blog. 2019. https://blog.jetbrains.com/pycharm/2019/04/collaboration-with-anaconda-inc/. Accessed 01 Feb 2023.

  25. Wang L, Wang X, Hawbani A, Xiong Y, Zhang X. An analysis of deep neural network models for image recognition applications. J Intell Fuzzy Syst. 2021. https://doi.org/10.3233/jifs-219081.

    Article  Google Scholar 

  26. Wali SB, Hannan MA, Hussain A, Samad SA. An automatic traffic sign detection and recognition system based on colour segmentation shape matching, and SVM. Math Probl Eng. 2015. https://doi.org/10.1155/2015/250461.

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Department of Software Engineering, Istanbul Aydin University, Isanbul, 34295, Turkey

    Sadaf Waziry

  2. Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, 34303, Turkey

    Jawad Rasheed & Harun Elkiran

  3. Faculty of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Fahad Mahmoud Ghabban

  4. College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, 11942, Al Kharj, Saudi Arabia

    Shtwai Alsubai & Abdullah Alqahtani

Authors
  1. Sadaf Waziry
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jawad Rasheed
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Fahad Mahmoud Ghabban
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Shtwai Alsubai
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Harun Elkiran
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Abdullah Alqahtani
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jawad Rasheed.

Ethics declarations

Conflict of Interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Waziry, S., Rasheed, J., Ghabban, F.M. et al. Unveiling Interpretability: Analyzing Transfer Learning in Deep Learning Models for Traffic Sign Recognition. SN COMPUT. SCI. 5, 682 (2024). https://doi.org/10.1007/s42979-024-03034-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-024-03034-6

Keywords