To determine the positions of marking in the presence of distracting shadows, highlight, pavement cracks, etc. RGB color space is transformed into I_1I_2I_3 color space and I_2 component was used to form a new image with less effect of the clutter. Using an improved edge detection operator, an edge strength map was produced, and binarilized by adaptive thresholds. The binary image was labeled and circularity of all connected components is calculated. The Self-Organizing Mapping is adopted to extract regions which imply potential marking. Finally the position of marking was obtained by curve fitting. Color information was utilized fully, all thresholds were set adaptively and lane marking could be detected in challenging images with shadows, highlight or other cars. The method based on circularity of connected components shows its outstanding robustness to lane marking detection and has a wide variety of applications in the areas of vehicle autonomous navigation and driver assistance system.
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