A Robust Vision-based Lane Detection using RANSAC Algorithm

In a pursuit to reduce ever increasing road accidents by developing an advanced driver assistance system (ADAS), this paper proposes a vision-based lane detection algorithm. This paper incorporates a framework constituting of color space conversion, region of interest (ROI), adaptive histogram equal...

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Bibliographic Details
Published in:2022 IEEE Global Conference on Computing, Power and Communication Technologies (GlobConPT) pp. 1 - 5
Main Authors: Sukumar, N., Sumathi, P.
Format: Conference Proceeding
Language:English
Published: IEEE 23.09.2022
Subjects:
Adaptive histogram equalization
Advanced driver assistance system
Clustering algorithms
Histograms
Image color analysis
Lane detection
Lighting
Road images
Roads
Visualization
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Summary:In a pursuit to reduce ever increasing road accidents by developing an advanced driver assistance system (ADAS), this paper proposes a vision-based lane detection algorithm. This paper incorporates a framework constituting of color space conversion, region of interest (ROI), adaptive histogram equalization, clustering of lane pixels, and RANdom SAmple Consensus (RANSAC) to develop a lane detection algorithm. The advantage of adaptive histogram equalization is to adjust the pixel intensity of Shadow and illumination regions in the road image using a contrast limit function. Further, clustering of a lane pixels is used to count and accumulate lane pixels above certain threshold. Finally, a RANSAC algorithm is applied to remove outliers and fit the lane lines model. The advantage of proposed framework is to detect the ego-lane and also all the lane boundaries in the image plane. Moreover, based on visual analysis, algorithm reveals a superior lane detection performance suitable for illumination variation, shadow, and lane variant width.
DOI:10.1109/GlobConPT57482.2022.9938320