基于极值区域增强的鲁棒道路车道检测

Jingchen Gu, Qieshi Zhang, S. Kamata
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引用次数: 8

摘要

道路车道检测是先进驾驶辅助系统(ADAS)中的一个关键问题。为了解决这一问题,基于视觉的检测方法得到了广泛的应用,并且通常侧重于边缘信息。然而,在各种路况下,仅使用边缘信息会导致缺失检测和错误检测。本文提出了一种基于邻域的图像转换方法,称为极值区域增强。该方法增强了白线的强度,对阴影和照度变化具有较强的鲁棒性。该方法同时提取白线的边缘和形状信息作为车道特征。此外,我们利用提取的特征实现了鲁棒的道路车道检测算法,并通过概率跟踪提高了算法的正确性。实验结果表明,该方法的平均检出率比现有方法提高了13.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust road lane detection using extremal-region enhancement
Road lane detection is a key problem in advanced driver-assistance systems (ADAS). For solving this problem, vision-based detection methods are widely used and are generally focused on edge information. However, only using edge information leads to miss detection and error detection in various road conditions. In this paper, we propose a neighbor-based image conversion method, called extremal-region enhancement. The proposed method enhances the white lines in intensity, hence it is robust to shadows and illuminance changes. Both edge and shape information of white lines are extracted as lane features in the method. In addition, we implement a robust road lane detection algorithm using the extracted features and improve the correctness through probability tracking. The experimental result shows an average detection rate increase of 13.2% over existing works.
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