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

Kodeeswari Manoharan; Philemon Daniel

doi:10.1117/1.JEI.26.6.063011

10 November 2017 Image processing-based framework for continuous lane recognition in mountainous roads for driver assistance system

Kodeeswari Manoharan, Philemon Daniel

Author Affiliations +

Funded by: Visvesvaraya PHD Scheme for Electronics and IT, Government of India

Journal of Electronic Imaging, Vol. 26, Issue 6, 063011 (November 2017). https://doi.org/10.1117/1.JEI.26.6.063011

Abstract

This paper presents a robust lane detection technique for roads on hilly terrain. The target of this paper is to utilize image processing strategies to recognize lane lines on structured mountain roads with the help of improved Hough transform. Vision-based approach is used as it performs well in a wide assortment of circumstances by abstracting valuable information contrasted with other sensors. The proposed strategy processes the live video stream, which is a progression of pictures, and concentrates on the position of lane markings in the wake of sending the edges through different channels and legitimate thresholding. The algorithm is tuned for Indian mountainous curved and paved roads. A technique of computation is utilized to discard the disturbing lines other than the credible lane lines and show just the required prevailing lane lines. This technique will consequently discover two lane lines that are nearest to the vehicle in a picture as right on time as could reasonably be expected. Various video sequences on hilly terrain are tested to verify the effectiveness of our method, and it has shown good performance with a detection accuracy of 91.89%.

Citation Download Citation

Kodeeswari Manoharan and Philemon Daniel "Image processing-based framework for continuous lane recognition in mountainous roads for driver assistance system," Journal of Electronic Imaging 26(6), 063011 (10 November 2017). https://doi.org/10.1117/1.JEI.26.6.063011

Received: 7 August 2017; Accepted: 20 October 2017; Published: 10 November 2017

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