Intelligent Vehicles Oriented Lane Detection Approach under Bad Road Scene

Shen, Huan; Li, Shunming; Bo, Fangchao; Miao, Xiaodong; Lü, Wei

doi:10.1109/cit.2009.25

Cited by 9 publications

(4 citation statements)

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“…This design ameliorates the edge detection algorithm, adopting the dynamic threshold value instead of the original static threshold. The formula to calculate the dynamic threshold value is shown in formula (1) [14][15][16][17][18].…”

Section: The Image Processing Of the Smart Tracing Carmentioning

confidence: 99%

The Design and Research of the Smart Path Tracing Car Based on the Image Recognition

Meng-yuan

2015

TOAUTOCJ

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To realize the smart car's automatic path tracing along the different types of path, a smart path tracing car based on car model is developed. In the design of the smart path tracing car, image processing affects the smart car's performance directly. This article adopts direct acquisition, direct edge detection algorithm and the improved edge detection algorithm to analyze and compare the road message acquired by COMS camera. Plenty of experiment data show that, under different light intensity, the edge detection algorithm can acquire image with better accuracy and efficiency, solving the contradiction between the system's rapidity and stability, and realizing the smart car's running along the guide curve stably and rapidly.

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Section: The Image Processing Of the Smart Tracing Carmentioning

confidence: 99%

The Design and Research of the Smart Path Tracing Car Based on the Image Recognition

Meng-yuan

2015

TOAUTOCJ

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“…For edge-based methods, the difficulty is to eliminate noise edges. Although the method presented in [9] is dedicated to this issue, but it is still insufficient. For histogram-based methods, they can work only on the scene without any vehicle or road sign in front of the car [13].…”

Section: Introductionmentioning

confidence: 97%

“…To deal with the problem related to [9], which still have many noise edges remaining after noise edges elimination process, we provide a novel solution which can eliminate noise edges more efficiently. In our system, computational complexity is another issue we take into consideration, so we present an adaptive region of interest (ROI) to increase the efficiency of our detection algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of this technique is to separate the lane-marks from different environment according to some features; however, most of the existing lane detection methods are sensitive to bad weather or shadow effect. Current research has shown various techniques to acquire these road markings, such as color-based methods [1] [2][3] [4][5] [6], edge-based methods [7][8] [9][10] [11], histogram-based methods [12] [13], perspective transformation based methods [14] [15], model-based methods [16]. However, color-based methods use a threshold for full image [2] or specific color information [3] to extract these land marks, but it may fail where shadow or bad weather effects.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Method for Lane-Mark Extraction in Complex Conditions

Chang

Lin

2012

2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Tr

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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.

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