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

PLoS One. 2017 Mar 6;12(3):e0173317. doi: 10.1371/journal.pone.0173317. eCollection 2017.

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).

MeSH terms

  • Algorithms
  • Automobile Driving*
  • Humans
  • Learning*
  • Models, Theoretical*
  • Pattern Recognition, Automated*

Grants and funding

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) (http://www.iitp.kr).