Robust Lane Detection using Two-stage Feature Extraction with Curve Fitting. (November 2016)

• Record Type: Journal Article
• Authors: Niu, Jianwei; Lu, Jie; Xu, Mingliang; Lv, Pei; Zhao, Xiaoke
• Is Part Of: Pattern recognition. Volume 59(2016:Nov.)
• Page Start: 225
• Page End: 233
• Publication Date: 2016-11
• Subjects: Lane detection -- Hough Transform -- Cluster -- Curve fitting; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.12.010
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: With the increase in the number of vehicles, many intelligent systems have been developed to help drivers to drive safely. Lane detection is a crucial element of any driver assistance system. At present, researchers working on lane detection are confronted with several major challenges, such as attaining robustness to inconsistencies in lighting and background clutter. To address these issues in this work, we propose a method named Lane Detection with Two-stage Feature Extraction (LDTFE) to detect lanes, whereby each lane has two boundaries. To enhance robustness, we take lane boundary as collection of small line segments. In our approach, we apply a modified HT (Hough Transform) to extract small line segments of the lane contour, which are then divided into clusters by using the DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering algorithm. Then, we can identify the lanes by curve fitting. The experimental results demonstrate that our modified HT works better for LDTFE than LSD (Line Segment Detector). Through extensive experiments, we demonstrate the outstanding performance of our method on the challenging dataset of road images compared with state-of-the-art lane-detection methods. Abstract : Highlights: We proposed a novel lane detection method. Our method regards lane boundary as collection of small line segments. We proposed a modified Hough Transform to detect small line segments. Small line segments are clustered based on our proposed similarity measurement. Removing interferential clusters depends on the balance of small line segments.