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http://dx.doi.org/10.3807/JOSK.2013.17.2.188

Lane Detection Algorithm for Night-time Digital Image Based on Distribution Feature of Boundary Pixels  

You, Feng (School of Civil Engineering and Transportation, South China University of Technology)
Zhang, Ronghui (Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences)
Zhong, Lingshu (School of Electronic and Information Engineering, South China University of Technology)
Wang, Haiwei (School of Civil Engineering and Transportation, South China University of Technology)
Xu, Jianmin (School of Civil Engineering and Transportation, South China University of Technology)
Publication Information
Journal of the Optical Society of Korea / v.17, no.2, 2013 , pp. 188-199 More about this Journal
Abstract
This paper presents a novel algorithm for nighttime detection of the lane markers painted on a road at night. First of all, the proposed algorithm uses neighborhood average filtering, 8-directional Sobel operator and thresholding segmentation based on OTSU's to handle raw lane images taken from a digital CCD camera. Secondly, combining intensity map and gradient map, we analyze the distribution features of pixels on boundaries of lanes in the nighttime and construct 4 feature sets for these points, which are helpful to supply with sufficient data related to lane boundaries to detect lane markers much more robustly. Then, the searching method in multiple directions- horizontal, vertical and diagonal directions, is conducted to eliminate the noise points on lane boundaries. Adapted Hough transformation is utilized to obtain the feature parameters related to the lane edge. The proposed algorithm can not only significantly improve detection performance for the lane marker, but it requires less computational power. Finally, the algorithm is proved to be reliable and robust in lane detection in a nighttime scenario.
Keywords
Lane detection; Hough transformation; Pattern Recognition; Machine vision; Intelligent transportation system;
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Times Cited By KSCI : 6  (Citation Analysis)
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