Traffic Sign Recognition with Light Convolutional Networks

Bo Xun Wu, Pin Yu Wang, Yi Ta Yang, Jiun-In Guo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this work, we aim to design a light net that can be executed on the embedded system in real time. We modify VGG Net to a small net, called Safe Net, and utilize multi-scale features for traffic sign recognition. Moreover, we convert the dataset into grayscale, which has been proved that has a better performance on GTSRB dataset. In addition, we augment the training data by about 6.6 times more via spinning, distorting and flipping to boost the accuracy. On NVIDIA Jetson TX1, Safe Net only takes 4.58ms per image including preprocessing at the testing and Safe Net can even achieve 99.34% accuracy.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538663011
DOIs
StatePublished - 27 Aug 2018
Event5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018 - Taichung, Taiwan
Duration: 19 May 201821 May 2018

Publication series

Name2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018

Conference

Conference5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
CountryTaiwan
CityTaichung
Period19/05/1821/05/18

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  • Cite this

    Wu, B. X., Wang, P. Y., Yang, Y. T., & Guo, J-I. (2018). Traffic Sign Recognition with Light Convolutional Networks. In 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018 [8448685] (2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-China.2018.8448685