A Novel Lane Line Detection Based on Multi-feature Fusion and Windows Searching

Lane detection embedded in intelligent vehicles can greatly improve the security of automatic driving. This work offers a new approach towards lane detection in the video in real-time combining multi-feature fusion and window searching. As the pre-procession, polygon filling is adopted to locate ROI (Region of Interest) in the video frames, which contain the lane-lines to be detected. To remove the backgrounds in the ROIs, we extract and fuse the features of color, histogram, and gradient of line lanes. Based on the density distribution of the pixels in the line lanes, the initial location is found by homography transformation. Then all candidate pixel points in the whole lane-line are extracted in the way of window searching. Finally, On the basis of the obtained lane-mark coordinates, a curve model is defined, and the model parameters are obtained by Least Square Estimation (LSE). Experimental results show the robustness and instantaneity of the proposed algorithm with the accuracy of 96% and the detecting time of only 20.7ms. In addition, lane-lines with misleading backdrops can also be detected such as yellow lane lines on the ground, shadow, bright light, lane-line defects and traffic light