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http://dx.doi.org/10.9717/kmms.2018.21.11.1244

Traffic Light Recognition Using a Deep Convolutional Neural Network  

Kim, Min-Ki (Dept. of Computer Science, Gyeongsang National University, Engineering Research Institute)
Publication Information
Journal of Korea Multimedia Society / v.21, no.11, 2018 , pp. 1244-1253 More about this Journal
Abstract
The color of traffic light is sensitive to various illumination conditions. Especially it loses the hue information when oversaturation happens on the lighting area. This paper proposes a traffic light recognition method robust to these illumination variations. The method consists of two steps of traffic light detection and recognition. It just uses the intensity and saturation in the first step of traffic light detection. It delays the use of hue information until it reaches to the second step of recognizing the signal of traffic light. We utilized a deep learning technique in the second step. We designed a deep convolutional neural network(DCNN) which is composed of three convolutional networks and two fully connected networks. 12 video clips were used to evaluate the performance of the proposed method. Experimental results show the performance of traffic light detection reporting the precision of 93.9%, the recall of 91.6%, and the recognition accuracy of 89.4%. Considering that the maximum distance between the camera and traffic lights is 70m, the results shows that the proposed method is effective.
Keywords
Traffic Light Recognition; Deep Convolutional Neural Network;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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