An Efficient Method for Lane-Mark Extraction in Complex Conditions

Abstract

Lane-mark detection is one of the most important parts in intelligent transportation systems (ITS). In this paper, we propose a lane-mark detection system which can overcome a lot of difficult situations, such as bad weather conditions, shadow effect, or road sign on the road. After the region of interest (ROI) of a road image is determined, we apply the Canny edge detector to investigate boundaries. In order to remove the noise edges, we divide the boundary image into sub-images to calculate local edge-orientation of each block and remove the edge with abnormal orientation. In this step, we produce a table to store the blocks which satisfy the assumption of lane-mark edge-orientation and use this information as an adaptive ROI of lane-marks. We propose the edge-pair scanning method to verify the edges which belong to lane-marks by using the relationship of adjacent edges of lane-marks and the width between these two edges. In the local adaptive threshold finding method, we also divide the image into sub-images and apply the feature that road lane-marks are always painted with high contrast colors with the road surface. Then, we use multi-adaptive thresholding method for each block. The system can work robustly under the situation that different parts of the image have different contrast for lane-marks. After eliminate the noise edges, we apply Hough Transform to fit the lane-marks as straight line models. The experiment results show that the proposed method can detect the lane-marks in real-time for various different environments.

Lane Line Detection based on Mask R-CNN

“…One is feature-based method [8][9][10][11], which can distinguish feature points of lane lines according to road characteristics such as color, gradient or edge. Chang ChinYu et al [8] applied a Canny edge detection operator to extract the boundary, and proposed an edge scanning method and Hough transform to verify whether the edge belongs to the lane line. In previous studies [9], they introduced an adaptive region of interest (ROI) and lane location method.…”

Lane detection algorithm based on temporal–spatial information matching and fusion

“…The basic idea of edge detection is to highlight the edge of the image locally using the edge enhancement operator and to extract the edge point set by setting the threshold. The commonly used detection operators include the differential operator, Laplacian operator, and canny operator [50, 51]. The canny edge detection operator is a relatively new edge detection operator that allows the following: Gaussian filtration, prediction of the magnitude, and direction of the gradient via the finite difference of the first‐order partial derivatives, non‐maxima suppression of the magnitude of the gradient, good edge detection and connection via the dual‐threshold algorithm, and better balance between edge detection and noise reduction.…”

Road Lane Detection by Discriminating Dashed and Solid Road Lanes Using a Visible Light Camera Sensor

“…Chang et al applied a canny edge detector to investigate boundaries and proposed an edge-pair scanning method and HT to verify that the edges belonged to lane markings [18]. In previous research [19], they had introduced a method for determining the adaptive road region-of-interest (ROI) and locating the road lane.…”