Lane Line Extraction in Raining Weather Images by Ridge Edge Detection with Improved MSR and Hessian Matrix

Abstract

To accurately detect lane lines in road traffic images at raining weather, a edge detection based method is studied, which mainly includes four algorithms. (1) Firstly an image is enhanced by an improved Retinex algorithm; (2) Then, an algorithm based on the Hessian matrix is applied to strengthen lane lines; (3) To extract the feature points of a lane line, a ridge edge detection algorithm based on five line detection in four directions is proposed, in which, in light on the possible positions of lane lines in the image, it detects the maximum gray level points in the local area of the detecting point within the pre-set valid detection region; and (4) After the noise removal based on the minimum circumscribed rectangles, the candidate points of lane lines are connected as segments, and for the gap filling between segments, in order to make connection correctly, the algorithm makes the filling in two steps, short gap and long gap fillings, and the long gap filling is made on the combination of segment angle difference and gap distance and gap angle. By testing hundreds of images of the lane lines at raining weather and by comparing several traditional image enhancement and segmentation algorithms, the new method of the lane line detection can produce the satisfactory results.

Lane line detection at nighttime on fractional differential and central line point searching with Fragi and Hessian

“…After the above image processing, the result image is a binary image, because the lane line edges are discontinuous in a binary image, the lane line maybe a group of line segments, to connect the segments, the standard Hough line transform algorithm is often used, but the results in some cases are not satisfactory. To overcome the shortages, some researchers have studied various straight line and curve fitting algorithms according to the characteristics of lane lines, for instance, Ozgunalp et al developed such an algorithm for tracing the lane lines based on the vanishing point estimation 19 ; Niu et al made the two-stage feature extraction with a curve fitting function 20 , and in 2021, we studied lane line detection in the raining weather with improved MSR and Hessian matrix for road image enhancement 21 . To well identify lane lines after the above image segmentation, the different versions of Hough transform are published, e.g., Fang, et al (2018) researched a lane line extraction algorithm on Hough Transform, in their Hough transform, the points conforming to the parallel characteristics, angle and length characteristics, and intercept characteristics of lane line are chosen in a Hough space, where, the selected points are converted into a lane line equation, and the final lane lines are conducted with fusion and property identification 22 ; Sun, et al (2019) made a multiple stage Hough Space computation for lane line detection 23 ; and Zhang and Ma (2019) did a lane line detection method by utilizing the Hough transform for the complex environments 24 .…”

Robust Stereo Road Image Segmentation Using Threshold Selection Optimization Method Based on Persistent Homology

“…Significant progress has been made in the field of autonomous driving in recent years, with stability-assisting functions such as lane line extraction [1], path finding [2] and multi-object recognition [3]. Stereo images can obtain richer information of various traffic elements such as disparity and depth in traffic scenes, and has the advantages of simple system structure and flexible operation, so the construction of stereo disparity map by stereo camera has gradually become a vision method with great development potential [4][5].…”