Real-time traffic sign recognition based on a general purpose GPU and deep-learningopen access
- Authors
- Lim, Kwangyong; Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran
- Issue Date
- Mar-2017
- Publisher
- PUBLIC LIBRARY SCIENCE
- Citation
- PLOS ONE, v.12, no.3, pp 1 - 22
- Pages
- 22
- Journal Title
- PLOS ONE
- Volume
- 12
- Number
- 3
- Start Page
- 1
- End Page
- 22
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/8630
- DOI
- 10.1371/journal.pone.0173317
- ISSN
- 1932-6203
- 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).
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