Fast lane detection with Randomized Hough Transform

Saudi, Azali; Teo, Jason; Hijazi, Mohd Hanafi Ahmad; Sulaiman, Jumat

doi:10.1109/itsim.2008.4631879

Cited by 26 publications

(9 citation statements)

References 7 publications

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“…A novel BLVS was introduced to enhance the computational efficiency of the method. Then, the validity of the line segment was checked by the line validation method proposed by Akinlar et al [21]. After all local regions were processed, we used a model selection function to choose an appropriate geometric model to piecewise fit the shape of the road.…”

Section: Overview Of Our Methodsmentioning

confidence: 99%

“…Sobel [16], Canny [17], Steer filter [14] and Garbor [18] filters are gradient-based and intensity-based edge point extraction methods. Based on the edge information extracted by these detectors, Hough transform [19,20,21,22], line segment detector (LSD) [23], edge drawing line detector (EDLines) [24] and fast line detector (FLD) [25] are then used to find straight lines in the image edge. The maximally stable extremal regions (MSER) method was proposed by Küçükmanisa et al [26,27] to solve lane detection problems in a complex environment, such as shadows or at night.…”

Section: Related Workmentioning

confidence: 99%

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Robust Lane-Detection Method for Low-Speed Environments

Zhou

et al. 2018

Sensors

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Vision-based lane-detection methods provide low-cost density information about roads for autonomous vehicles. In this paper, we propose a robust and efficient method to expand the application of these methods to cover low-speed environments. First, the reliable region near the vehicle is initialized and a series of rectangular detection regions are dynamically constructed along the road. Then, an improved symmetrical local threshold edge extraction is introduced to extract the edge points of the lane markings based on accurate marking width limitations. In order to meet real-time requirements, a novel Bresenham line voting space is proposed to improve the process of line segment detection. Combined with straight lines, polylines, and curves, the proposed geometric fitting method has the ability to adapt to various road shapes. Finally, different status vectors and Kalman filter transfer matrices are used to track the key points of the linear and nonlinear parts of the lane. The proposed method was tested on a public database and our autonomous platform. The experimental results show that the method is robust and efficient and can meet the real-time requirements of autonomous vehicles.

show abstract

Section: Overview Of Our Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Robust Lane-Detection Method for Low-Speed Environments

Zhou

et al. 2018

Sensors

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“…Points in parameter space represent particular instances of the curve in the image. Therefore, the strategy used by the Hough transform is to map sets of points from a particular instance of the considered line to a single point representing the line in Hough space and, in effect, cause a peak to occur at that point [10].…”

Section: The Detection Algorithm Based On Hough Transformmentioning

confidence: 99%

A New Lane Line Segmentation and Detection Method based on Inverse Perspective Mapping

Li¹,

Zhao²,

Hou³

et al. 2011

JDCTA

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Lane line detection plays an important role in vehicle autonomous driving, however non lane line markings and vehicle shadows seriously influence lane line detection results. In this paper, we propose a new lane line segmentation and detection algorithm based on inverse perspective mapping (IPM). Firstly, by using the restriction of the consistent lane line width after IPM, we segment the lane line and project the segmentation results back to the original coordinate system; then detect the right and left two lane lines by the gray scale weighted Hough transform method. Experimental results show that the proposed algorithm has strong robustness to non lane line markings and vehicle shadows, and the lane line can be accurately detected under the complex pavement conditions.

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“…Moreover, color representation cannot comprehensively disclose lane marking features so that its use should be in combination with other non-color features such as lane edges or corners, painted lines, etc. [ 29 , 30 , 31 ]. The authors in [ 32 ] analyzed low-level features by using an adaptive segmentation method, and then an efficient line segment detector was proposed for lane marking detection.…”

Section: Introductionmentioning

confidence: 99%

Lane Marking Detection and Reconstruction with Line-Scan Imaging Data

Luo

Wang

2018

Sensors

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Lane marking detection and localization are crucial for autonomous driving and lane-based pavement surveys. Numerous studies have been done to detect and locate lane markings with the purpose of advanced driver assistance systems, in which image data are usually captured by vision-based cameras. However, a limited number of studies have been done to identify lane markings using high-resolution laser images for road condition evaluation. In this study, the laser images are acquired with a digital highway data vehicle (DHDV). Subsequently, a novel methodology is presented for the automated lane marking identification and reconstruction, and is implemented in four phases: (1) binarization of the laser images with a new threshold method (multi-box segmentation based threshold method); (2) determination of candidate lane markings with closing operations and a marching square algorithm; (3) identification of true lane marking by eliminating false positives (FPs) using a linear support vector machine method; and (4) reconstruction of the damaged and dash lane marking segments to form a continuous lane marking based on the geometry features such as adjacent lane marking location and lane width. Finally, a case study is given to validate effects of the novel methodology. The findings indicate the new strategy is robust in image binarization and lane marking localization. This study would be beneficial in road lane-based pavement condition evaluation such as lane-based rutting measurement and crack classification.

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Fast lane detection with Randomized Hough Transform

Cited by 26 publications

References 7 publications

Robust Lane-Detection Method for Low-Speed Environments

Robust Lane-Detection Method for Low-Speed Environments

A New Lane Line Segmentation and Detection Method based on Inverse Perspective Mapping

Lane Marking Detection and Reconstruction with Line-Scan Imaging Data

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