Skip to main content
SpringerLink
Log in

Evaluation of Deep Learning Algorithms for Traffic Sign Detection to Implement on Embedded Systems

  • Chapter
  • First Online:
Recent Advances of Hybrid Intelligent Systems Based on Soft Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 915))

Abstract

Nowadays, machine learning algorithms are trendy and are used to solve different problems of autonomous vehicles obtaining good results. Among these algorithms, deep learning has emerged as an excellent alternative to improve the results of the state-of-the-art in machine vision applications. An essential task in autonomous vehicles is the detection of traffic signs. Some metrics used for these detectors focus on assessing precision and recall. However, it is necessary to consider other factors, such as the implementation of these models on an embedded system. In this work, we implement deep learning algorithms on an embedded system to evaluate two different detection algorithms: Faster R-CNN and Single Shot Multibox Detector (SSD) with two feature extractors, ResNet V1 101 and MobileNet V1 to determine the location of traffic signs within the observed scenario. The contribution of this work focuses on evaluating the implementation of traffic sign detection systems based on deep learning algorithms on embedded systems. The experiments were achieved on the experimental embedded system board Nvidia Jetson Nano. The inference time and memory consumption of these detection systems were evaluated; they delivered good performance (81–98%) measure by average precision for each superclass (prohibitory, warning, and mandatory).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

References

  1. G.E. Hinton, What kind of graphical model is the brain? in Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (Morgan Kaufmann Publishers Inc, 2005), pp 1765–1775

    Google Scholar 

  2. R. Girshick, Fast R-CNN. in 2015 IEEE International Conference on Computer Vision (ICCV), 7–13 Dec 2015 (2015), pp. 1440–1448. https://doi.org/10.1109/iccv.2015.169

  3. B. Oyster, ABI Research Forecasts 8 Million Vehicles to Ship with SAE Level 3, 4 and 5 Autonomous Technology in 2025 (2018). ABI research. http://ABI Research: for Visionaries. www.abiresearch.com/press/abi-research-forecasts-8-million-vehicles-ship-sae-level-3-4-and-5-autonomous-technology-2025

  4. Nvidia, Jetson Nano. https://developer.nvidia.com/embedded/jetson-nano (2019)

  5. J. Shuttleworth, SAE J3016 automated-driving graphic. https://www.sae.org/news/2019/01/sae-updates-j3016-automated-driving-graphic (2019)

  6. M. Lynberg, Automated Vehicles for Safety. https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety (2019)

  7. D.G. Costa, Visual sensors hardware platforms: a review. IEEE Sens. J. 20(8), 4025–4033 (2020). https://doi.org/10.1109/JSEN.2019.2952447

    Article  Google Scholar 

  8. K. Panetta, 5 Trends Appear on the Gartner Hype Cycle for Emerging Technologies, 2019. https://www.gartner.com/smarterwithgartner/5-trends-appear-on-the-gartner-hype-cycle-for-emerging-technologies-2019/ (2020)

  9. P. Sermanet, Y. LeCun, Traffic sign recognition with multi-scale Convolutional Networks, in The 2011 International Joint Conference on Neural Networks, 31 July-5 Aug 2011 (2011). Pp. 2809–2813. https://doi.org/10.1109/ijcnn.2011.6033589

  10. A. Malinowski, H. Yu, Comparison of embedded system design for industrial applications. IEEE Trans. Indus. Inf. 7(2), 244–254 (2011). https://doi.org/10.1109/TII.2011.2124466

    Article  Google Scholar 

  11. N. Yadav, U. Binay, Comparative Study of Object Detection Algorithms (2017)

    Google Scholar 

  12. M. Swathi, K.V. Suresh, Automatic traffic sign detection and recognition: a review, in 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET), 16–18 Feb 2017 (2017). pp. 1–6. https://doi.org/10.1109/icammaet.2017.8186650

  13. Y. Saadna, A. Behloul, An overview of traffic sign detection and classification methods. Int. J. Mult. Inf. Ret. 6(3), 193–210 (2017). https://doi.org/10.1007/s13735-017-0129-8

    Article  Google Scholar 

  14. W.G. Hatcher, W. Yu, A survey of deep learning: platforms, applications and emerging research trends. IEEE Access 6, 24411–24432 (2018). https://doi.org/10.1109/ACCESS.2018.2830661

    Article  Google Scholar 

  15. X. Bangquan, W.X. Xiong, Real-time embedded traffic sign recognition using efficient convolutional neural network. IEEE Access 7, 53330–53346 (2019). https://doi.org/10.1109/ACCESS.2019.2912311

    Article  Google Scholar 

  16. J. Salvi, X. Armangué, J. Batlle, A comparative review of camera calibrating methods with accuracy evaluation. Pattern Recogn. 35(7), 1617–1635 (2002). https://doi.org/10.1016/S0031-3203(01)00126-1

    Article  MATH  Google Scholar 

  17. G.E. Hinton, S. Osindero, Y.-W. Teh, A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006). https://doi.org/10.1162/neco.2006.18.7.1527

    Article  MathSciNet  MATH  Google Scholar 

  18. R. Raina, A. Madhavan, Ng AY large-scale deep unsupervised learning using graphics processors, in 26th Annual International Conference, 2009. (ACM Press, Montreal, Quebec, Canada, 2009), pp 1–8. https://doi.org/10.1145/1553374.1553486

  19. A. Mogelmose, M.M. Trivedi, T.B. Moeslund, Vision-based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans. Intell. Trans. Syst. 13(4), 1484–1497 (2012). https://doi.org/10.1109/TITS.2012.2209421

    Article  Google Scholar 

  20. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  21. A. Canziani, A. Paszke, E. Clurciello, An analysis of deep neural network models for practical applications. https://arxiv.org/abs/1605.07678 (2016)

  22. A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386

    Article  Google Scholar 

  23. Á. Arcos-García, J.A. Álvarez-García, L.M. Soria-Morillo, Evaluation of deep neural networks for traffic sign detection systems. Neurocomputing 316, 332–344 (2018). https://doi.org/10.1016/j.neucom.2018.08.009

    Article  Google Scholar 

  24. Avramović A, Tabernik D, Skočaj D Real-time Large Scale Traffic Sign Detection. In: 2018 14th Symposium on Neural Networks and Applications (NEUREL), 20–21 Nov. 2018 2018. pp 1-4. https://doi.org/10.1109/neurel.2018.8587013

  25. M. Loni, M. Daneshtalab, M. Sjödin, ADONN: adaptive design of optimized deep neural networks for embedded systems, in 2018 21st Euromicro Conference on Digital System Design (DSD), 29–31 Aug. 2018 (2018), pp. 397–404. https://doi.org/10.1109/dsd.2018.00074

  26. B. Yoshua, L. Pascal, P. Dan, L. Hugo, Greedy layer-wise training of deep networks, in: Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems (Vancouver, British Columbia, Canada, 4–7 Dec 2006. MIT Press, 2006), pp. 153–160

    Google Scholar 

  27. M.A. Ranzato, C. Poultney, S. Chopra, Y. LeCun, Efficient learning of sparse representations with an energy-based model. in Advances in Neural Information Processing Systems 19. (MIT Press, 2007), pp 1137–1144

    Google Scholar 

  28. C. Rödel, S. Stadler, A. Meschtscherjakov, M. Tscheligi, Towards autonomous cars: the effect of autonomy levels on acceptance and user experience, in 6th International Conference, 2014. (ACM Press, Seattle, WA, USA, 2014), pp 1–8. https://doi.org/10.1145/2667317.2667330

  29. J.M. Lillo-Castellano, I. Mora-Jiménez, C. Figuera-Pozuelo, J.L. Rojo-Álvarez, Traffic sign segmentation and classification using statistical learning methods. Neurocomputing 153, 286–299 (2015). https://doi.org/10.1016/j.neucom.2014.11.026

    Article  Google Scholar 

  30. M. Yang, B. Li, H. Fan, Y. Jiang, Randomized spatial pooling in deep convolutional networks for scene recognition, in 2015 IEEE International Conference on Image Processing (ICIP), 27–30 Sep 2015. pp 402–406. https://doi.org/10.1109/icip.2015.7350829

  31. Y. Yang, H. Luo, H. Xu, F. Wu, Towards real-time traffic sign detection and classification. IEEE Trans. Intell. Transp. Syst. 17(7), 2022–2031 (2016). https://doi.org/10.1109/TITS.2015.2482461

    Article  Google Scholar 

  32. K. He, X. Zhang, S. Ren, S. Jian, Deep residual learning for image recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27–30 June 2016. pp 770–778. https://doi.org/10.1109/cvpr.2016.90

  33. S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017). https://doi.org/10.1109/TPAMI.2016.2577031

    Article  Google Scholar 

  34. K. Lim, Y. Hong, Y. Choi, H. Byun, Real-time traffic sign recognition based on a general purpose GPU and deep-learning. PLoS ONE 12(3), e0173317 (2017). https://doi.org/10.1371/journal.pone.0173317

    Article  Google Scholar 

  35. V.K. Kukkala, J. Tunnell, S. Pasricha, T. Bradley, Advanced driver-assistance systems: a path toward autonomous vehicles. IEEE Consum. Electron. Maga. 7(5), 18–25 (2018). https://doi.org/10.1109/MCE.2018.2828440

    Article  Google Scholar 

  36. L.-M. Miguel, R. Yoshio, S.-A. Moisés, O.-R. Ulises, Evaluation of algorithms for traffic sign detection, in Proceedings of SPIE (2019). https://doi.org/10.1117/12.2529709

  37. M. Everingham, L. Van Gool, C.K.I. Williams, J. Winn, A. Zisserman, The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010). https://doi.org/10.1007/s11263-009-0275-4

    Article  Google Scholar 

  38. R. Qian, Z. Bailing, Y. Yong, W. Zhao, F. Coenen, Robust chinese traffic sign detection and recognition with deep convolutional neural network, in 2015 11th International Conference on Natural Computation (ICNC), 15–17 Aug 2015, pp. 791–796. https://doi.org/10.1109/icnc.2015.7378092

  39. J. Xu, B. Wang, J. Li, C. Hu, J. Pan, Deep learning application based on embedded GPU, in 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS), 3–5 June 2017, pp 1–4. https://doi.org/10.1109/eiis.2017.8298723

  40. B. Taylor, V.S. Marco, W. Wolff, Y. Elkhatib, Z. Wang, Adaptive deep learning model selection on embedded systems. SIGPLAN Not 53(6), 31–43 (2018). https://doi.org/10.1145/3299710.3211336

    Article  Google Scholar 

  41. T. Yang, X. Long, A.K. Sangaiah, Z. Zheng, C. Tong, Deep detection network for real-life traffic sign in vehicular networks. Comput. Netw. 136, 95–104 (2018). https://doi.org/10.1016/j.comnet.2018.02.026

    Article  Google Scholar 

  42. X. Feng, Y. Jiang, X. Yang, M. Du, X. Li, Computer vision algorithms and hardware implementations: a survey. Integration 69, 309–320 (2019). https://doi.org/10.1016/j.vlsi.2019.07.005

    Article  Google Scholar 

  43. Y. Zhu, C. Zhang, D. Zhou, X. Wang, X. Bai, W. Liu, Traffic sign detection and recognition using fully convolutional network guided proposals. Neurocomputing 214, 758–766 (2016). https://doi.org/10.1016/j.neucom.2016.07.009

    Article  Google Scholar 

  44. F. Shao, X. Wang, F. Meng, T. Rui, D. Wang, J. Tang, Real-time traffic sign detection and recognition method based on simplified gabor wavelets and CNNs. Sensors 18(10), 3192 (2018). https://doi.org/10.3390/s18103192

    Article  Google Scholar 

  45. L. Liu, W. Ouyang, X. Wang, P. Fieguth, J. Chen, X. Liu, M. Pietikäinen, Deep learning for generic object detection: a survey. Int. J. Comput. Vision 128(2), 261–318 (2020). https://doi.org/10.1007/s11263-019-01247-4

    Article  Google Scholar 

  46. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, Berg AC SSD: single shot MultiBox detector, in Computer Vision—ECCV 2016, eds by B. Leibe, J. Matas, N. Sebe, M. Welling. (Springer International Publishing, 2016), pp. 21–37

    Google Scholar 

  47. A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017). arXiv:170404861 [cs]

  48. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, C.L. Zitnick, Microsoft COCO: common objects in context, in Computer Vision—ECCV 2014. eds by D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Springer, Cham, 2014), pp. 740–755

    Google Scholar 

  49. J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, K. Murphy, Speed/accuracy trade-offs for modern convolutional object detectors, in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21–26 2017. (2017) pp. 3296–3297. https://doi.org/10.1109/cvpr.2017.351

  50. J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, T. Chen, Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018). https://doi.org/10.1016/j.patcog.2017.10.013

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by Instituto Politécnico Nacional (institutional project SAPI 2020053) and by the Mexican National Council of Science and Technology (CONACYT, Mexico). We thank the Coordinación Institucional de Investigación of CETYS Universidad for their collaboration.

Author information

Authors and Affiliations

  1. Instituto Politécnico Nacional, CITEDI-IPN Av, Instituto Politécnico Nacional, No. 1310, Nueva Tijuana, C.P. 22435, Tijuana, Baja California, Mexico

    Miguel Lopez-Montiel, Moisés Sánchez-Adame & Oscar Montiel

  2. Centro de Innovación y Diseño (CEID), CETYS Universidad, Av. No 4., Fracc. El Lago, C.P 22210, Tijuana, Baja California, Mexico

    Ulises Orozco-Rosas & Kenia Picos

Authors
  1. Miguel Lopez-Montiel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ulises Orozco-Rosas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Moisés Sánchez-Adame
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kenia Picos
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Oscar Montiel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oscar Montiel .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Lopez-Montiel, M., Orozco-Rosas, U., Sánchez-Adame, M., Picos, K., Montiel, O. (2021). Evaluation of Deep Learning Algorithms for Traffic Sign Detection to Implement on Embedded Systems. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Recent Advances of Hybrid Intelligent Systems Based on Soft Computing. Studies in Computational Intelligence, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-58728-4_5

Download citation

Publish with us

Policies and ethics

Search

Navigation