심층 합성곱 신경망을 이용한 교통신호등 인식

Traffic Light Recognition Using a Deep Convolutional Neural Network

초록

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.

키워드

Traffic Light Recognition; Deep Convolutional Neural Network
제목
심층 합성곱 신경망을 이용한 교통신호등 인식
제목 (타언어)
Traffic Light Recognition Using a Deep Convolutional Neural Network
저자
김민기
DOI
10.9717/kmms.2018.21.11.1244
발행일
2018
저널명
멀티미디어학회논문지
권
21
호
11
페이지
1244 ~ 1253