hree-lane Detection Method Based on TopHat Segmentation and Curve Models

In order to solve problems in the process of traditional three-lane detection, such as low anti-interference capacity, inaccuracy in lane fitting, and error identification of side lane, a three-lane detection method was proposed in this paper based on TopHat segmentation and curve models. A lane segmentation algorithm using variable-kernel TopHat was proposed as image pre-processing, by using shape features and color features of lane markings. For lane detection, firstly, a vanish-point fitting method based on WLS (weighted least squares) was proposed as a constraint of Hough transform. Secondly, straight lines were clustered in polar coordinates by using DBSCAN (density-based spatial clustering of applications with noise), matching to a template. Then lane ROI (region of interest) was made according to the straight-line template, and the lane was searched and fitted by using cubic curve. Finally, for uncertain side-lanes, a side-lane driving judgement method was proposed by using random seed. The algorithm has better performance in both detection rate and lane miss rate than traditional three-lane detection algorithms. Experiment verifies that the method has high accuracy and stability, and is useful for three-lane detection.