一种基于高斯径向基函数网络的基于颜色的车道检测方法

P. Chanawangsa, Chang Wen Chen
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引用次数: 8

摘要

车道检测在智能交通系统中起着核心作用。在过去,对强度图像进行边缘检测已经得到了广泛的应用,但它通常会产生带有噪声的二值图像。最值得注意的是,可能为车道检测提供重要线索的场景颜色信息没有得到真正的考虑。本文提出了一种新的基于颜色的车道检测系统。尽管基于颜色的方案有其公平的问题,包括不同的照明条件,通过依赖于从高斯径向基函数(GRBF)网络的离线监督训练中获得的车道标记颜色预测器,这些问题可以适当地克服。实验结果表明,与主要基于边缘的方法相比,该方法可以有效地消除结构良好的场景中不属于车道标记的错误边缘。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Color-Based Lane Detection Via Gaussian Radial Basis Function Networks
Lane detection plays a central role in intelligent transportation systems. While edge detection on intensity images has gained much popularity in the past, it usually results in noisy binary images. Most noticeably, the color information of the scene that may provide an important cue for lane detection has not been genuinely considered. In this paper, we propose a novel color-based lane detection system. Although color-based schemes have their fair share of issues, including varying illumination conditions, by relying on a lane mark color predictor obtained from an offline supervised training of Gaussian radial basis function (GRBF) networks, such issues can be appropriately overcome. Experimental results have demonstrated that the proposed approach, in contrast to predominantly edge-based approaches, can effectively eliminate erroneous edges that do not belong to the lane marks in well-structured scenes.
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