基于可变形模板和遗传算法的道路识别新方法

Tie Liu, N. Zheng, Hong Cheng, Zhengbei Xing
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引用次数: 16

摘要

基于视觉导航的道路识别是智能汽车研究的一个重要课题。道路图像通常会受到阴影、噪声和道路轮廓不连续性的影响,这使得传统的基于边缘的算法鲁棒性大大降低。为了避免阈值选择的负面影响,本文提出了一种基于可变形模板和遗传算法的道路识别算法。首先利用边缘算子对道路图像进行预处理,获取边缘信息,然后构造道路轮廓的可变形模板模型及其似然函数,该似然函数定义了给定模板可变形参数的拟合程度,最后利用遗传算法搜索似然函数的全局最大值,得到道路轮廓可变形模板模型的最优参数。实验结果表明,该算法对道路轮廓的阴影、噪声和不连续性具有较强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel approach of road recognition based on deformable template and genetic algorithm
Road recognition based on vision navigation is an important task in intelligent vehicle research. Road image is usually influenced by shadows, noise and discontinuity of road contour, and this induces the traditional edge-based algorithm's robustness decrease greatly. To avoid negative influence from threshold selection, this paper presents a deformable template and genetic algorithm based road recognition algorithm. Firstly, preprocess road image with edge operator to get the edge information, then construct a deformable template model of road contour and its likelihood function which define the fitting degree for a given template deformable parameter, finally genetic algorithm is used to search the global maximal value of the likelihood function to get the optimal parameter of the deformable template model of road contour. Experimental results indicate that the algorithm has strong robustness for shadows, noise and discontinuity of road contour.
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