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 journalArticle

24 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

All Science Journal Classification (ASJC) codes

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

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