Abstract
Lane detection is a challenging problem. It has attracted the attention of the computer vision community for several decades. Essentially, lane detection is a multifeature detection problem that has become a real challenge for computer vision and machine learning techniques. Although many machine learning methods are used for lane detection, they are mainly used for classification rather than feature design. But modern machine learning methods can be used to identify the features that are rich in recognition and have achieved success in feature detection tests. However, these methods have not been fully implemented in the efficiency and accuracy of lane detection. In this paper, we propose a new method to solve it. We introduce a new method of preprocessing and ROI selection. The main goal is to use the HSV colour transformation to extract the white features and add preliminary edge feature detection in the preprocessing stage and then select ROI on the basis of the proposed preprocessing. This new preprocessing method is used to detect the lane. By using the standard KITTI road database to evaluate the proposed method, the results obtained are superior to the existing preprocessing and ROI selection techniques.
Highlights
With the rapid development of society, automobiles have become one of the transportation tools for people to travel
In 2016, Mammeri et al used progressive probabilistic Hough transform combined with maximum stable extreme area (MSER) technology to identify and detect lane lines and utilized Kalman filter to achieve continuous tracking [15]
Experiments show that the proposed method is significantly better than the existing preprocessing method and ROI selection method in lane detection
Summary
With the rapid development of society, automobiles have become one of the transportation tools for people to travel. Advanced driver assistance systems which include lane departure warning (LDW) [3], Lane Keeping Assist, and Adaptive Cruise Control (ACC) [4] can help people analyse the current driving environment and provide appropriate feedback for safe driving or alert the driver in dangerous circumstances. This kind of auxiliary driving system is expected to become more and more perfect [5]. In the complex traffic environment where vehicles are numerous and speed is too fast, the probability of accidents is much greater than usual. In such a complex traffic situation, road colour extraction and texture detection as well as road boundary and lane marking are the main perceptual clues of human driving [7]
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