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Traffic Sign Recognition for Self-driving Cars with Deep Learning

  • Daniel XieEmail author
  • Emmanuel Nuakoh
  • Prosenjit Chatterjee
  • Ashkan Ghattan
  • Kossi Edoh
  • Kaushik Roy
Conference paper
  • 65 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1141)

Abstract

体育赛事投注记录the purpose of this research was to create a model for an autonomous car in traffic sign recognition. a high-accuracy model is needed to analyze the signs. previous studies have mainly been centered on european countries, and the models created in europe are not applicable to american autonomous cars. the contributions of this paper are twofold. first, this study generated a dataset that was collected and annotated in order to establish a suitable model for the usa. the dataset was custom made and acquired by using camera footage that was converted into individual frames. the dataset was named cyber identity and biometrics lab traffic sign dataset version 1 (cib ts v1). then, it was annotated into different classes and labels with labelimg. with a customized program, we used the annotations to crop out images and categorized them. second, the data was run through a deep learning algorithm called modified alexnet. a lighter version of the alexnet was used for our experiments. results showed that the model achieved above 99% accuracy on the validation set.

Keywords

Autonomous car Traffic sign recognition Deep learning Image data collection 

Notes

Acknowledgements

体育赛事投注记录we would like to acknowledge the support from the national security agency (grant #h98230-19-1-0012), the national science foundation (nsf), and grant hrd #1719498.

References

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    DFG Consulting d.o.o.: DFG traffic sign data set (n.d.). Accessed
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    Møgelmose, A., Trivedi, M., Moeslund, T.: Vision based traffic sign detection and analysis for intelligent driver assistance systems: perspectives and survey. IEEE Trans. Intell. Transp. Syst. 13(I.4) (2012)
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    Tabernik, D., Skocaj, D.: (2019). Accessed
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    Forson, E.: Recognising traffic signs with 98% accuracy using deep learning (2017). Accessed
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    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (2012)
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    Shao, F., Wang, X., Meng, F., Rui, T., Wang, D., Tang, J.: Real-time traffic sign detection and recognition method based on simplified Gabor wavelets and CNNs. Sensors (Basel) 10 (2018).  
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Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Daniel Xie
    • 1
    Email author
  • Emmanuel Nuakoh
    • 2
  • Prosenjit Chatterjee
    • 2
  • Ashkan Ghattan
    • 2
  • Kossi Edoh
    • 3
  • Kaushik Roy
    • 2
  1. 1.North Carolina School of Science and MathDurhamUSA
  2. 2.Department of Computer ScienceNC A&T State UniversityGreensboroUSA
  3. 3.Department of MathematicsNC A&T State UniversityGreensboroUSA

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