[논문]Real Time Road Lane Detection with RANSAC and HSV Color Transformation

Kim, Kwang Baek; Song, Doo Heon

doi:10.6109/jicce.2017.15.3.187

Real Time Road Lane Detection with RANSAC and HSV Color Transformation 원문보기

Journal of information and communication convergence engineering, v.15 no.3, 2017년, pp.187 - 192

Kim, Kwang Baek (Division of Computer Software Engineering, Silla University) , Song, Doo Heon (Department of Computer Games, Yong-In SongDam College)

Abstract ▼ AI-Helper

Autonomous driving vehicle research demands complex road and lane understanding such as lane departure warning, adaptive cruise control, lane keeping and centering, lane change and turn assist, and driving under complex road conditions. A fast and robust road lane detection subsystem is a basic but important building block for this type of research. In this paper, we propose a method that performs road lane detection from black box input. The proposed system applies Random Sample Consensus to find the best model of road lanes passing through divided regions of the input image under HSV color model. HSV color model is chosen since it explicitly separates chromaticity and luminosity and the narrower hue distribution greatly assists in later segmentation of the frames by limiting color saturation. The implemented method was successful in lane detection on real world on-board testing, exhibiting 86.21% accuracy with 4.3% standard deviation in real time.

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가설 설정

ii) An incorrect detection occurs when the estimate is overlaid on something other than a lane marker.

제안 방법

For the road lane detection problem that is a basic component of autonomous vehicle research, vision-based approaches are the most natural and cost-effective paradigms. In this paper, we demonstrate our implementation of a fast and robust real-time road lane detection method using RANSAC and other image processing algorithms under the HSV color model.
One study combines input from multiple cameras and line models are formed from feature points using a RANSAC algorithm, although the method supports real-time operation only at low speeds [12]. Other studies construct a computational model of the ego-lane geometry by fitting candidate lane markings to the model parameters using RANSAC [13] or propose an improved RANSAC algorithm which is combined with least square analysis to calculate the lane model parameters with an edge detection algorithm [14] under different road condition assumptions.

성능/효과

The basic understanding of fast and accurate identification of road lanes is still an important task. And, in this paper, we demonstrated that the accuracy of the proposed method (86.21% average accuracy) is acceptable in realworld experiments that use black box videos as input. However, tracking algorithms and other sources of information will be required to handle more complex driving scenarios.
Table 1 summarizes the experimental results for successful lane detection rate. The successful extraction rate is distributed between 79.23% and 93.87% with an average of 86.21% and standard deviation of 4.30%.

참고문헌 (17)

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A. Borkar, M. Hayes, and M. Smith, "Robust lane detection and tracking with RANSAC and Kalman filter," in Proceedings of 2009 16th IEEE International Conference on Image Processing (ICIP), Cairo, Egypt, pp. 3261-3264, 2009.
A. B. Hillel, R. Lerner, D. Levi, and G. Raz, "Recent progress in road and lane detection: a survey," Machine Vision and Applications, vol. 25, no. 3, pp. 727-745, 2014.

상세보기
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상세보기
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상세보기
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상세보기
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상세보기
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M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of ACM, vol. 24, no. 6, pp. 381-395, 1981.

상세보기
Z. Kim, "Robust lane detection and tracking in challenging scenarios," IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 1, pp. 16-26, 2008.

상세보기
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상세보기
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Real Time Road Lane Detection with RANSAC and HSV Color Transformation 원문보기

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Real Time Road Lane Detection with RANSAC and HSV Color Transformation 원문보기

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