Real-time traffic sign recognition based on a general purpose GPU and deep-learning

Kwangyong Lim, Yongwon Hong, Yeongwoo Choi, Hyeran Byun

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)

Abstract

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based realtime traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).

Original languageEnglish
Article numbere0173317
JournalPloS one
Volume12
Issue number3
DOIs
Publication statusPublished - 2017 Mar

Bibliographical note

Funding Information:
This work was supported by an Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean government (MSIP) (no. R7117-16-0157, Development of Smart Car Vision Techniques based on Deep Learning for Pedestrian Safety).

Publisher Copyright:
© 2017 Lim et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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