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Traffic Sign Recognition Based on Joint Convolutional Neural Network Model

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Published:28 August 2019Publication History

ABSTRACT

This paper takes the automatic driving technology as the research background, and studies the algorithm and model of traffic sign recognition. Traffic sign recognition is the basis of automatic driving. This paper takes common traffic signs as the research object, and uses the current international standard traffic sign image database GTSRB as the data set of this paper.

According to the development status of deep learning and image recognition technology in recent years, this paper analyzes and compares several different image recognition models in ImageNet competition. Based on these experimental results, a new joint network model is proposed, which overcomes some The shortcomings of the existing model, using the model to test on the GTSRB data set, can be found that the model has faster training convergence speed and better recognition accuracy for the GTSRB data set.

References

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  2. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012:1097--1105.Google ScholarGoogle Scholar
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  5. Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning[J]. 2016.Google ScholarGoogle Scholar
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  1. Traffic Sign Recognition Based on Joint Convolutional Neural Network Model

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      cover image ACM Other conferences
      ICBDT '19: Proceedings of the 2nd International Conference on Big Data Technologies
      August 2019
      382 pages
      ISBN:9781450371926
      DOI:10.1145/3358528

      Copyright © 2019 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 28 August 2019

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