Details

Autor(en) / Beteiligte

• Lim, Kwangyong
• Hong, Yongwon
• Choi, Yeongwoo
• Byun, Hyeran
• Ma, Xiaolei

Titel

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

Ist Teil von

• PloS one, 2017-03, Vol.12 (3), p.e0173317-e0173317

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2017

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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).