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

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  • Kwangyong Lim
  • Yongwon Hong
  • Yeongwoo Choi
  • Hyeran Byun

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 real-time 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).

Suggested Citation

  • Kwangyong Lim & Yongwon Hong & Yeongwoo Choi & Hyeran Byun, 2017. "Real-time traffic sign recognition based on a general purpose GPU and deep-learning," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-22, March.
    • Handle: RePEc:plo:pone00:0173317
    DOI: 10.1371/journal.pone.0173317
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