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- Title
An Efficient Color Space for Deep-Learning Based Traffic Light Recognition.
- Authors
Kim, Hyun-Koo; Park, Ju H.; Jung, Ho-Youl
- Abstract
Traffic light recognition is an essential task for an advanced driving assistance system (ADAS) as well as for autonomous vehicles. Recently, deep-learning has become increasingly popular in vision-based object recognition owing to its high performance of classification. In this study, we investigate how to design a deep-learning based high-performance traffic light detection system. Twomain components of the recognition system are investigated: the color space of the input video and the networkmodel of deep learning. We apply six color spaces (RGB, normalized RGB, Ruta's RYG, YCbCr, HSV, and CIE Lab) and three types of network models (based on the Faster R-CNN and R-FCN models). All combinations of color spaces and network models are implemented and tested on a traffic light dataset with 1280×720 resolution. Our simulations show that the best performance is achieved with the combination of RGB color space and Faster R-CNN model. These results can provide a comprehensive guideline for designing a traffic light detection system.
- Subjects
TRAFFIC signs &; signals; DEEP learning; AUTONOMOUS vehicles; SIMULATION methods &; models; PHOTODETECTORS
- Publication
International Journal of Aerospace Engineering, 2018, p1
- ISSN
1687-5966
- Publication type
Article
- DOI
10.1155/2018/2365414