Image processing-based framework for continuous lane recognition in mountainous roads for driver assistance system

Manoharan, Kodeeswari; Daniel, Philemon

doi:10.1117/1.jei.26.6.063011

Cited by 12 publications

(2 citation statements)

References 15 publications

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“…Neural-network-based techniques have high robustness but impose difficulties in acquiring training information [60]. Several techniques used monocular vision sensor to extract the road area by employing specific attributes based on the road appearance [42,[61][62][63][64][65]. Although these techniques perform well in certain environments, there is a lack of effectiveness when the roads do not adequately correspond to the models of a priori distinct features [46].…”

Section: Discussionmentioning

confidence: 99%

Survey on various lane and driver detection techniques based on image processing for hilly terrain

Manoharan

Daniel

2018

IET Image Processing

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Section: Discussionmentioning

confidence: 99%

Survey on various lane and driver detection techniques based on image processing for hilly terrain

Manoharan

Daniel

2018

IET Image Processing

Self Cite

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“…Fitting or aggregation classes are used to draw the lane lines. [5][6][7][8][9] As vehicles and other object shadows produce noisy edges on the lane image, they should be removed by model fitting. As detection methods have evolved, many new methods have been proposed based on lane edge and lane marker color detection.…”

Section: Introductionmentioning

confidence: 99%

A novel deep learning network for accurate lane detection in low-light environments

Song

Chen

Liu

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Lane detection algorithms play a key role in Advanced Driver Assistance Systems (ADAS), which are however unable to achieve accurate lane recognition in low-light environments. This paper presents a novel deep network structure, namely LLSS-Net (low-light images semantic segmentation), to achieve accurate lane detection in low-light environments. The method integrates a convolutional neural network for low-light image enhancement and a semantic segmentation network for lane detection. The image quality is firstly improved by a low-light image enhancement network and lane features are then extracted using semantic segmentation. Fast lane clustering is finally performed by using the KD tree models. Cityscapes and Tusimple datasets are utilized to demonstrate the robustness of the proposed method. The experimental results show that the proposed method has an excellent performance for lane detection in low-light roads.

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Autonomous lane detection on hilly terrain for perception-based navigation applications

Manoharan

Daniel

2019

The Imaging Science Journal

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Image processing-based framework for continuous lane recognition in mountainous roads for driver assistance system

Cited by 12 publications

References 15 publications

Survey on various lane and driver detection techniques based on image processing for hilly terrain

Survey on various lane and driver detection techniques based on image processing for hilly terrain

A novel deep learning network for accurate lane detection in low-light environments

Autonomous lane detection on hilly terrain for perception-based navigation applications

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