Robust Lane Detection using Two-stage Feature Extraction with Curve Fitting
Robust Lane Detection using Two-stage Feature Extraction with Curve Fitting
复制标题
使用两阶段特征提取和曲线拟合的稳健车道检测
DOI：
10.1016/j.patcog.2015.12.010
复制
发表时间：
2016-11-01
影响因子：
8
通讯作者：
Zhao, Xiaoke
中科院分区：
文献类型：
--
作者：
Niu, Jianwei;Lu, Jie;Zhao, Xiaoke
随着汽车数量的增加，许多智能系统被开发出来，以帮助驾驶员安全驾驶。车道检测是任何驾驶员辅助系统的关键要素。目前，研究人员在车道检测工作面临着几个主要的挑战，如获得鲁棒性的不一致的照明和背景杂波。为了解决这些问题，在这项工作中，我们提出了一种名为车道检测与两阶段特征提取（LDTFE）来检测车道，其中每个车道有两个边界。为了提高鲁棒性，我们把车道边界看作是小线段的集合。在我们的方法中，我们应用修改后的HT（Hough变换）提取小线段的车道轮廓，然后划分成集群使用DBSCAN（基于密度的空间聚类的应用程序与噪声）聚类算法。然后，我们可以识别车道的曲线拟合。实验结果表明，我们的修改HT工程"更好地为LDTFE比LSD（线段检测器）。通过大量的实验，我们证明了我们的方法具有挑战性的道路图像数据集相比，国家的最先进的车道检测方法的出色表现。(C)2015爱思唯尔有限公司版权所有。
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. (C) 2015 Elsevier Ltd. All rights reserved.