Lane detection based on illumination invariant image

This paper aims at overcoming the defect caused by illumination variations and shadows in the feature extraction of lane detection. Illumination invariant angle is obtained with entropy-minimization method and it leads to a 1D, gray-scale image representation which is illumination invariant at each image pixel in log-chromaticity space. By finding line elements in Canny edge map, trivial edges in shadow sections are eliminated. Then, an improved voting scheme Hough transform is adopted to detect lines and the lane boundaries is represented with piecewise linear road model. The experimental results show the efficiency of proposed method in terms of invariance illumination, shadow removal, reliability and adaptability detection and real-time navigation.