A robust lane detection algorithm based on differential excitation

Since lane information is necessary for road security improvement in unmanned vehicle systems, the detection of the lane is an important task. Most existing approaches are particularly designed for specific road scenario (such as the highway, urban roads). However, the detection precision may be deteriorated if the lane markings are blurred and vestigial. Similarly, the reflection and smudges on the road surface also influence the detection result. In this paper, we introduce a novel robust lane detection algorithm based on the differential excitation. Firstly, we extract the region of interest (ROI) by considering human visual attention. Then we enhance salient texture information and remove the noise effectively through differential excitation. The binary image is obtained using Weber's law. Furthermore, we select the points that satisfy the proposed rules as voting points. Finally, the lane markings are detected and extracted by Hough transform accordingly. Experimental results on an open database indicate that the proposed method outperforms the classical Sebdani's approach and the Low's approach.