Road Lane Detection Using H-Maxima and Improved Hough Transform

Ghazali, Kamarul Hawari; Xiao, Rui; Ma, Jie

doi:10.1109/cimsim.2012.31

Cited by 51 publications

(26 citation statements)

References 9 publications

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“…Most of the existing lane detection studies can be divided into two groups, namely feature‐based methods and model‐based methods. Feature‐based lane detection system normally recognises the lane markings by using lane marking features such as the colour, texture, and the edges features [7–11]. In this case, the input images are normally processed with contrast enhancement and edge detection techniques; after that, the lane markings in the image can be detected using thresholding method or line detection algorithm such as Hough transform (HT) [11].…”

Section: Introductionmentioning

confidence: 99%

“…Feature‐based lane detection system normally recognises the lane markings by using lane marking features such as the colour, texture, and the edges features [7–11]. In this case, the input images are normally processed with contrast enhancement and edge detection techniques; after that, the lane markings in the image can be detected using thresholding method or line detection algorithm such as Hough transform (HT) [11]. Even though feature‐based lane detection algorithm can efficiently detect the lanes under some ideal condition, it will be less robust or even unable to find the lane markings when facing low quality lane markings.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic integration and online evaluation of vision‐based lane detection algorithms

Xing

Wang

et al. 2018

IET Intelligent Transport Systems

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic integration and online evaluation of vision‐based lane detection algorithms

Xing

Wang

et al. 2018

IET Intelligent Transport Systems

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“…Linear-parabolic lane models are proposed for lane departure warning systems, in which the linear function and quadratic function are used to model the lane markings in the near field and far field, respectively [ 33 , 37 ]. Hough Transform (HT) and its variants (e.g., improved HT, randomized HT, hierarchical HT) are widely used for straight or curved lane marking detection [ 2 , 38 , 39 , 40 , 41 ]. However, one primary limitation of this method is how to model arbitrary road shape.…”

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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“…The algorithm presented in this paper gives good experimental results which outperforms typically the most famous algorithms DBSCAN and OPTICS in all the standard benchmark datasets and it is generally able to deal with clusters of different densities. Kamarul Ghazali et al 2012 [5] The lane detection method depending on improved Hough Transform algorithm as well as H-MAXIMA transformation is presented in this paper. An image processing programming language platform is used to develop the technique and then was tested on collected video data.…”

Section: Related Workmentioning

confidence: 99%

A Review on the Artificial Bee Colony & Clahe Based Hybrid Hough Transform For Efficient Lane Detection

Multani

Marwaha²

2017

IJECS

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This paper represents that lane coloration has become popular in real time vehicular ad-hoc system. Lane detection is normally helpful to localize path limits, determine undesired lane variations, and to enable approximation of the upcoming geometry of the road. There are different types of methods that are used for detecting lines, curves and ellipses i.e. hough transform, clustering and curve fitting. The overall objective of this paper is to providing better performance in lane coloration algorithm. The methods developed till now are working effectively as well as giving good results.

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Road Lane Detection Using H-Maxima and Improved Hough Transform

Cited by 51 publications

References 9 publications

Dynamic integration and online evaluation of vision‐based lane detection algorithms

Dynamic integration and online evaluation of vision‐based lane detection algorithms

Lane Marking Detection and Reconstruction with Line-Scan Imaging Data

A Review on the Artificial Bee Colony & Clahe Based Hybrid Hough Transform For Efficient Lane Detection

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