Real-Time Lane Departure Detection Based on Extended Edge-Linking Algorithm

Lin, Qing; Han, Youngjoon; Hahn, Hernsoo

doi:10.1109/iccrd.2010.166

Cited by 76 publications

(37 citation statements)

References 8 publications

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“…Firstly, the determination of the region of interest (ROI) can reduce the complexity and the unnecessary information caused by noises. Isolating the ROI from other region before the computation [6] can reduce processing time while keeping a good performance. The fixed size of ROI may not work well, so some adaptive road ROI determination algorithms are proposed to detect the road region using the positional information of a vanishing point [7] or the position of the heading vehicles [4].…”

Section: Introductionmentioning

confidence: 99%

“…This topic can be separated into three essential blocks: lane marking detection, lane tracking and lane departure warning. For the lane marking detection algorithm, the existing solutions can be classified as feature-based [1,3,4], model-based [2,5] and region-based [1,6] methods. Among these methods, different feature-based methods for Lane detection are still the most popular and commonly used ways which contribute to its efficiency in multiple algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Robust and Real-Time Lane Marking Detection for Embedded System

Guo

Zhang

et al. 2015

Lecture Notes in Computer Science

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Abstract. Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane detection system based on a novel lane feature extraction approach. The robustness and real-time of algorithm enable different configurations of embedded solutions. The system is divided into three phases. Firstly, using the Prewitt operator we can get the rich useful details and using Shen Jun operator we can get step edge, on the other hand Shen Jun operator is the best filter to detect the symmetrical markings according to the maximum signal noise ratio (SNR) criterion. So we introduce the best compromise method between noise smoothing and edge locating that combining the Prewitt operator with Shen Jun operator to extract lane markings. Then a fast Hough transform based on image pyramid is applied to get the lane lines. The posterior algorithm of reasonably refining the Lane lines angle is introduced to correct to error caused by Hough transform. Finally, robust detection of vehicle's departure warning is also discussed. Experiment results on real road will be presented to prove the robustness and effectiveness of the proposed lane detection algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust and Real-Time Lane Marking Detection for Embedded System

Guo

Zhang

et al. 2015

Lecture Notes in Computer Science

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“…Qing et al [10] proposed the extended edge linking algorithm with directional edge gap closing. The new edge could be obtained with the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Lane Detection Based on Connection of Various Feature Extraction Methods

Jiang

2018

Advances in Multimedia

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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.

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“…Lin et al [15] proposed a method based on region of interest (ROI). The ROI first initialized and Sobel operator along with non-local maximum suppression is used to find the edge pixels.…”

Section: Lane Detection and Tracking Algorithmsmentioning

confidence: 99%