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Road Sign Identification with Convolutional Neural Network Using TensorFlow

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Machine Learning for Networking (MLN 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12629))

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Abstract

With the use and continuous development of deep learning methods, the recognition of images and scenes captured from the real environment has also undergone a major transformation in the techniques and parameters used. In most of the methods, we notice that recognition is based on extraction. This paper proposes a classification technique based on convolutional features in the context of Traffic Sign Detection and Recognition (TSDR) which uses an enriched dataset of traffic signs. This solution offers an additional level of assistance to the driver, allowing better safety of passengers, road users, and cars. An experimental evaluation on publicly available scene image datasets with convolutional features presents results with an accuracy of 94.7% of our classification model.

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References

  1. Bui-Minh, T., Ghita, O., Whelan, P.F., Hoang, T.: A robust algorithm for detection and classification of traffic signs in video data. In: 2012 International Conference on Control, Automation and Information Sciences (ICCAIS), Ho Chi Minh City, pp. 108–113 (2012)

    Google Scholar 

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

    Article  Google Scholar 

  3. Gu, J., et al.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018)

    Article  Google Scholar 

  4. Wu, Y., Li, Z., Chen, Y., Nai, K., Yuan, J.: Real-time traffic sign detection and classification towards real traffic scene. Multimed. Tools Appl. 79, 18201–18219 (2020)

    Google Scholar 

  5. De La Escalera, A., Armingol, J.M., Mata, M.: Traffic sign recognition and analysis for intelligent vehicles. Image Vis. Comput. 21(3), 247–258 (2003)

    Article  Google Scholar 

  6. Broggi, A., Cerri, P., Medici, P., Porta, P.P., Ghisio, G.: Real time road signs recognition. In: IEEE Intelligent Vehicles Symposium, Istanbul, 2007, pp. 981-986 (2007)

    Google Scholar 

  7. Piccioli, G., De Micheli, E., Parodi, P., Campani, M.: A robust method for road sign detection and recognition. Image Vis. Comput. J. 14(3), 209–223 (1996)

    Article  Google Scholar 

  8. Rosin, P.L.: Measuring shape: ellipticity, rectangularity, and triangularity. Mach. Vis. Appl. MVA. 1, 952–955 (2003)

    Google Scholar 

  9. Habib, A., Ha, M.: Hypothesis generation of instances of road signs in color imagery captured by mobile mapping systems. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 36, 159–165 (2007)

    Google Scholar 

  10. Garrido, M., Sotelo, M., Martm-Gorostiza, E.: Fast traffic sign detection and recognition under changing lighting conditions. In: IEEE Intelligent Transportation Systems Conference (ITSC 2006), pp. 811–816 (2006)

    Google Scholar 

  11. Belaroussi, R., Tarel, J.-P.: A real-time road sign detection using bilateral Chinese transform. In: Bebis, G. (ed.) ISVC 2009. LNCS, vol. 5876, pp. 1161–1170. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10520-3_111

    Chapter  Google Scholar 

  12. Loy, G., Zelinsky, A.: Fast radial symmetry for detecting points of interest. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 959–973 (2003)

    Article  Google Scholar 

  13. Loy, G., Barnes, N.: Fast shape-based road sign detection for a driver assistance system. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sendai, 2004, vol. 1, pp. 70–75 (2004)

    Google Scholar 

  14. Gavrila, D.M., Philomin, V.: Real-time object detection for “smart” vehicles. In: The Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, vol. 1, pp. 87–93 (1999)

    Google Scholar 

  15. Aoyagi, Y., Asakura, T.: A study on traffic sign recognition in scene image using genetic algorithms and neural networks. In: 22 nd IEEE international Conference on Industrial Electronics, Control and Instrumentation (IECON), Taiwan, vol. 3, pp. 1838–1843 (1996)

    Google Scholar 

  16. Foucher, P., Charbonnier, P., Kebbous, H.: Evaluation of a road sign pre-detection system by image analysis. In: The fourth International conference on computer vision theory and applications (VISAPP 2009), Lisbonne, pp. 362–367 (2009)

    Google Scholar 

  17. Lafuente-Arroyo, S., Gil-Jimenez, P., Maldonado-Bascon, R., Lopez-Ferreras, F., Maldonado-Bascon, S.: Traffic sign shape classification evaluation I: SVM using distance to borders. In: IEEE Intelligent Vehicles Symposium (IV 20O5), Las Vegas, pp. 557–562 (2005)

    Google Scholar 

  18. Ishizuka, Y., Hirai, Y.: Segmentation of road sign symbols using opponent-color filters. In: 11th World Congress on Intelligent Transport Systems and Services (ITSWC), Japan, pp. 18–22 (2004)

    Google Scholar 

  19. Serna, C.G., Ruichek, Y.: Classification of traffic signs: the European dataset. IEEE Access. 6, 78, 136–78, 148 (2018)

    Google Scholar 

  20. Simon, L., Tarel, J.P., Brémond, R.: Towards the estimation of conspicuity with visual priors. In: Third International Conference on Computer Vision Theory and Applications (VISAPP 2008), Portugal, pp. 323–328 (2008)

    Google Scholar 

  21. Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition. Neural Netw. 32, 323–332 (2012)

    Google Scholar 

  22. Aghdam, H., Heravi, E.J., Puig, D.: A practical and highly optimized convolutional neural network for classifying traffic signs in real-time. Int. J. Comput. Vision 122, 246–269 (2017)

    Article  MathSciNet  Google Scholar 

  23. Ruta, A., Li, Y., Liu, X.: Robust class similarity measure for traffic sign recognition. IEEE Trans. Intell. Transp. Syst. 11, 846–855 (2010)

    Article  Google Scholar 

  24. Raj, K.T.I., Raj, R.G.: Real-time (vision-based) road sign recognition using an artificial neural network. Sensors 17, 853 (2017). Mdpi

    Google Scholar 

  25. Lau, M.M., Lim, K.H., Gopalai, A.A.: Malaysia traffic sign recognition with convolutional neural network. In: IEEE International Conference on Digital Signal Processing (DSP), Singapore, pp. 1006–1010 (2015)

    Google Scholar 

  26. Li, Y., Møgelmose, A., Trivedi, M.M.: Pushing the ‘speed limit’: high-accuracy US traffic sign recognition with convolutional neural networks. IEEE Trans. Intell. Veh. 1, 167–176 (2016)

    Article  Google Scholar 

  27. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, Columbus, pp. 580–587 (2014)

    Google Scholar 

  28. Li, J., Wang, Z.: Real-time traffic sign recognition based on efficient CNNs in the wild. IEEE Trans. Intell. Transp. Syst. 20, 975–984 (2019)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. LME, Hassiba Ben Bouali University, Chlef, Algeria

    Mohammed Kherarba, Mounir Tahar Abbes, Mohammed Meddah & Abdelhak Benhamada

  2. CNAM/CEDRIC, Paris, France

    Selma Boumerdassi

  3. University of Oran1, Oran, Algeria

    Mohammed Senouci

Authors
  1. Mohammed Kherarba
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  2. Mounir Tahar Abbes
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  3. Selma Boumerdassi
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  4. Mohammed Meddah
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  5. Abdelhak Benhamada
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  6. Mohammed Senouci
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Corresponding author

Correspondence to Selma Boumerdassi .

Editor information

Editors and Affiliations

  1. Laboratoire LIGM UMR 8049 CNRS, ESIEE Paris, Noisy-le-Grand, France

    Éric Renault

  2. CNAM/CEDRIC, Paris, France

    Selma Boumerdassi

  3. Inria/EVA Project, Paris, France

    Paul Mühlethaler

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Kherarba, M., Abbes, M.T., Boumerdassi, S., Meddah, M., Benhamada, A., Senouci, M. (2021). Road Sign Identification with Convolutional Neural Network Using TensorFlow. In: Renault, É., Boumerdassi, S., Mühlethaler, P. (eds) Machine Learning for Networking. MLN 2020. Lecture Notes in Computer Science(), vol 12629. Springer, Cham. https://doi.org/10.1007/978-3-030-70866-5_17

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  • DOI: https://doi.org/10.1007/978-3-030-70866-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70865-8

  • Online ISBN: 978-3-030-70866-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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