Structure Lane Detection Based on Saliency Feature of Color and Direction

Abstract

In this paper, we propose a method for structure lane detection; the method is based on two features: color and direction. This method can improve the robustness and accuracy of lane detection. Two kinds of saliency map have been calculated: color saliency map and direction saliency map. The final saliency map is the combination of the two map mentioned above. The binary image is getting from the final saliency map, and the feature points which used for fitting have been selected. The road region is segmented by the lanes. Experiment result shows that the proposed method produces better performance against some other methods.

A Lane Detection Method Based on a Ridge Detector and Regional G-RANSAC

“…Currently, there are two main methods to detect lane-division-lines: feature-based detection and model-based detection. Feature-based detection extracts the edge location distribution, connected shadow area, color and texture differences from graphs to detect lane-division-lines [1,2,3]. Zhang proposed a Hough transform based fitting-lane method for tracking [4].…”