Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Traffic Light Recognition Using a Deep Convolutional Neural Network

심층 합성곱 신경망을 이용한 교통신호등 인식

  • Kim, Min-Ki (Dept. of Computer Science, Gyeongsang National University, Engineering Research Institute)
  • Received : 2018.08.07
  • Accepted : 2018.10.18
  • Published : 2018.11.30
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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

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Fig. 1. Overall process for creating an image of lighting regions.

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Fig. 4. Samples of the training data.

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Fig. 5. Input images of the DCNN classifier.

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Fig. 2. (a) input image, (b) ROI image (c) binary image B1, (d) binary image B2.

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Fig. 3. (a) size filtered image F2, (b) merged image IF.

Table 1. Each class size of the training data

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Table 2. Architecture of the DCNN classifier

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Table 3. Ground truth of test data

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Table 4. Performance of traffic light detection

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Table 5. Performance of traffic light recognition

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Table 6. Performance of the DCNN using augmented data

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Table 7. Performance comparison of traffic light recognition

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