The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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A New Efficient Detection Method in Lane Road Environment

도로 환경에 효율적인 새로운 차선 검출 방법

  • Received : 2018.01.22
  • Accepted : 2018.02.22
  • Published : 2018.02.28
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Abstract

In this paper, we propose a new real-time lane detection method that is efficient for road environment. Existing methods have a problem of low reliability under environmental changes. In order to overcome this problem, we emphasize the lane candidate area by using gray level division. And Extracts a straight line component near the lane by using the Hough transform, and generates an ROI for each straight line based on the extracted coordinates. And integrates the generated ROI images. Then, the lane is determined by dividing the object using the dual queue in the ROI image. The proposed method is able to detect lanes even in the environmental change unlike the conventional method. And It is possible to obtain an advantage that the area corresponding to the background such as sky, mountain, etc. is efficiently removed and high reliability is obtained.

본 논문은 도로 환경에 효율적인 새로운 차선 검출 방법을 제안한다. 기존 차선 검출 방법은 환경적인 변화 속에서 낮은 신뢰성의 문제가 있다. 이러한 문제를 보완하기 위해 그레이 레벨 분할을 이용하여 차선 후보가 되는 영역을 강조한다. 그리고 허프 변환을 이용하여 차선에 가까운 영역의 직선 성분을 추출하고 추출 된 좌표를 기반으로 각 직선마다 ROI 생성한다. 그리고 생성 된 ROI 이미지를 논리연산으로 통합한다. 그리고 ROI 이미지에 이중큐를 이용한 객체 분할로 차선을 결정한다. 제안하는 방법은 기존과 다르게 환경적인 변화에도 차선 검출이 가능하였으며, 하늘, 산 등 배경에 해당하는 영역을 효율적으로 제거하는 장점과 높은 신뢰성을 얻을 수 있다.

Keywords

References

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