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A High-performance Lane Recognition Algorithm Using Word Descriptors and A Selective Hough Transform Algorithm with Four-channel ROI

다중 ROI에서 영상 화질 표준화 및 선택적 허프 변환 알고리즘을 통한 고성능의 차선 인식 알고리즘

  • 조재현 (울산대학교 전기전자공학부) ;
  • 장영민 (울산대학교 전기전자공학부) ;
  • 조상복 (울산대학교 전기전자공학부)
  • Received : 2014.12.29
  • Accepted : 2015.02.05
  • Published : 2015.02.25

Abstract

The examples that used camera in the vehicle is increasing with the growth of the automotive market, and the importance of the image processing technique is expanding. In particular, the Lane Departure Warning System (LDWS) and related technologies are under development in various fields. In this paper, in order to improve the lane recognition rate more than the conventional method, we extract a Normalized Luminance Descriptor value and a Normalized Contrast Descriptor value, and adjust image gamma values to modulate Normalized Image Quality by using the correlation between the extracted two values. Then, we apply the Hough transform using the optimized accumulator cells to the four-channel ROI. The proposed algorithm was verified in 27 frame/sec and $640{\times}480$ resolution. As a result, Lane recognition rate was higher than the average 97% in day, night, and late-night road environments. The proposed method also shows successful lane recognition in sections with curves or many lane boundary.

자동차 시장의 성장과 함께 차량에 카메라가 사용되는 사례가 늘고 있으며 영상 처리 기술의 중요성이 증가하고 있다. 또한, 차량 전장 시스템 기술 역시 급속도로 성장을 하고 있으며, 특히 차선이탈경보시스템(Lane Departure Warning System, LDWS)과 관련된 기술들이 다방면으로 개발 중이다. 본 논문에서는 기존의 방법보다 더 높은 차선 인식률을 검출하기 위해 촬영된 영상에서 먼저 Normalized Luminance Descriptor와 Normalized Contrast Descriptor값을 각각 연산하여, 두 값의 상관관계를 통해 Normalized Image Quality값을 조절하여 영상의 감마값을 조절한다. 그 뒤 다중의 관심영역을 통해 다중 영역에서 선택적 허프변환 알고리즘을 통한 차선 검출 알고리즘을 적용하여 차량 전방의 차선을 인식한다. 제안하는 알고리즘은 평균 27 Frame/sec와 $640{\times}480$ 해상도에서 검증 과정을 가졌다. 결과적으로 주 야간 및 심야를 포함한 도로들에서 평균 97% 이상의 차선 인식률을 보였으며 커브구간이나 차도 내 표식이 많은 구간에서도 성공적인 차선 인식을 보인다.

Keywords

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