Lane detection is a challenging problem. It has attracted the attention of the computer vision community for several decades.\nEssentially, lane detection is a multi feature detection problem that has become a real challenge for computer vision and machine\nlearning techniques. Although many machine learning methods are used for lane detection, they are mainly used for classification\nrather than feature design. But modern machine learning methods can be used to identify the features that are rich in recognition\nand have achieved success in feature detection tests. However, these methods have not been fully implemented in the efficiency\nand accuracy of lane detection. In this paper, we propose a new method to solve it.We introduce a new method of preprocessing\nand ROI selection.The main goal is to use the HSV colour transformation to extract the white features and add preliminary edge\nfeature detection in the preprocessing stage and then select ROI on the basis of the proposed preprocessing.This newpreprocessing\nmethod is used to detect the lane. By using the standard KITTI road database to evaluate the proposedmethod, the results obtained\nare superior to the existing preprocessing and ROI selection techniques.
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