Real-time traffic sign recognition based on a general purpose GPU and deep-learning
By Kwangyong Lim, Yongwon Hong, Yeongwoo Choi, Hyeran Byun. 2017
79 citations recorded by the source corpus.
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).
Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition
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In Civil and Structural Engineering
In Computer Vision and Pattern Recognition
Citations in the corpus
- Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “[126] proposed a real-time traffic sign detection based on CNN that has been integrated with a general purpose GPU.”