Existing lane detection algorithms come with costs regarding the required memory and the complexity of the algorithms limiting ways of generating versions adaptive to challenging road environments and portable low-power consumption equipments. We propose a new solution for lane detection based on geometric moment sampling. First, the current frame of a recorded video is processed by selecting potential regions of interest (i.e., potentially containing lane markings) based on piecewise sampling. Then, the visible road surface is segmented by labeling connected components of equivalent pixel values using an image binarization procedure. By analyzing the different order of geometric moments of connected components in the lane region, the centroid and the direction angle of detected parts of lane marking segments are calculated. We combine these calculated parameters; a lane marking segment is finally detected via piecewise curve fitting to the lane marking segment. The algorithm is verified on synthetic and real-word images. Results show that the proposed method not only has real-time performance and a high accuracy in detecting lanes of varying appearance, but also offers convenient adaptation of detection both to light-reflective road surface and further types of lighting disturbances.