Lane Departure Identification for Advanced Driver Assistance

Gaikwad, Vijay; Lokhande, Shashikant

doi:10.1109/tits.2014.2347400

Cited by 99 publications

(95 citation statements)

References 25 publications

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“…1, the previous edge-based lane detection algorithm conventionally consists of two steps; Canny edge detection [7], and Hough transform [8]. During Canny edge detection, the input gray-scaled image is firstly passed through several filters to reduce the noise level (Gaussian masking) and to find the gradient information.…”

Section: Previous Algorithmsmentioning

confidence: 99%

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Diagonally-reinforced Lane Detection Scheme for High-performance Advanced Driver Assistance Systems

Park¹,

Yoo²,

Park³

et al. 2017

JSTS:Journal of Semiconductor Technology and Science

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Abstract-In this paper, several optimizations are proposed to enhance the quality of lane detection algorithms in automotive applications. Considering the diagonal directions of lanes, the proposed limited Hough transform newly introduces image-splitting and angle-limiting schemes that relax the number of possible angles at the line voting process. In addition, unnecessary edges along the horizontal and vertical directions are pre-defined and removed during the edge detection procedures, increasing the detecting accuracy remarkably. Simulation results shows that the proposed lane recognition algorithm achieves an accuracy of more than 90% and a computing speed of 92 frame/sec, which are superior to the results from the previous algorithms.

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Section: Previous Algorithmsmentioning

confidence: 99%

“…1. After finding the detailed edge information, Hough transform is used to detect the specific lane [8]. More precisely, each edge position generates several pairs of θ and d, which denote all the possible lines including the corresponding point.…”

Section: Previous Algorithmsmentioning

confidence: 99%

Diagonally-reinforced Lane Detection Scheme for High-performance Advanced Driver Assistance Systems

Park¹,

Yoo²,

Park³

et al. 2017

JSTS:Journal of Semiconductor Technology and Science

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“…OpenCV is an open source computer vision library.The library is written in C and C++ and runs under Linux,which provides great convenience for image processing.Its powerful image and matrix operation ability shorten the development cycle for the developers [2].The OpenCVlibrary contains over 500 functions that span many areas invision including factory product inspection, medicalimaging, security, and user inter face, camera calibration,stereo vision and robotics.…”

Section: Opencv Overviewmentioning

confidence: 99%

Research on Lane Detection Technology Based on OPENCV

Xu¹,

Zhang²

2015

Proceedings of the 3rd International Conference on Mechanical Engineering and Intelligent Systems (ICMEIS 2015)

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Abstract. In view of the huge computing, poor anti-interference ability of traditional detection alogrithm, it does not meet the requirement of the vehicle system, for which this paper proposed a lane detection method based on OpenCV. Preprocessing image in the OpenCV environment, adopting LMedSquare(Least Median Square) idea to select the best subset combined with least squares method to picewise fitting the lane so that it realized automatic identification of lane. This algorithm is suitable for both straight and curve. Simulation shows that this algorithm has well real-time performance, accuracy and robustness. It can meet the requirements of the vehicle system.

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“…It is worth mentioning that most lane detection methods are based on the Hough transform (Leng, 2010), , (Tan, 2014), (Gaikwad, 2015), a well-known technique used to detect lines and curves (Duda, 1972). This kind of line detectors strongly depend on the pre-processing stage; they are typically combined with a Canny or Sobel edge detector or with an adaptive threshold.…”

Section: Introductionmentioning

confidence: 99%

Efficient Lane Detection Based on Artificial Neural Networks

Arce

Zamora

Hernández

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Lane detection is a problem that has attracted in the last years the attention of the computer vision community. Most of approaches used until now to face this problem combine conventional image processing, image analysis and pattern classification techniques. In this paper, we propose a methodology based on so-called Ellipsoidal Neural Networks with Dendritic Processing (ENNDPs) as a new approach to provide a solution to this important problem. The functioning and performance of the proposed methodology is validated with a real video taken by a camera mounted on a car circulating on urban highway of Mexico City.

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Lane Departure Identification for Advanced Driver Assistance

Cited by 99 publications

References 25 publications

Diagonally-reinforced Lane Detection Scheme for High-performance Advanced Driver Assistance Systems

Diagonally-reinforced Lane Detection Scheme for High-performance Advanced Driver Assistance Systems

Research on Lane Detection Technology Based on OPENCV

Efficient Lane Detection Based on Artificial Neural Networks

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