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首页> 外文会议>2017 International Conference on Security, Pattern Analysis, and Cybernetics >Optimized generalized hough transform for road marking recognition application
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Optimized generalized hough transform for road marking recognition application

机译：优化的广义霍夫变换在道路标记识别中的应用

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摘要

The road markings recognition is an important research in the field of driverless cars. The generalized Hough transform (GHT) is effective for detecting and recognizing contour objects as road markings. While the precision rate of GHT is not very high in applications. This paper presents an edge-type based generalized Hough transform (ETGHT). The edge-type is obtained by multiple thresholds partition of a proposed edge feature and is recorded by multiple R-tables. The edge feature is calculated by a breadth first search strategy using the location and gradient direction of the edge points. In application, a road marking recognition framework based on ETGHT is presented. First, an edge extraction method based on differential excitation is used to obtain the image contours. Then the edge-type feature of the edge points of input image is extracted to determine the corresponding R-table. In the voting stage, a peak region screening processing is used to improve the system's precision rate. Experimental results have shown that the proposed method provides significant improvement of precision rate while ensuring the recall rate.
机译：道路标记识别是无人驾驶汽车领域的重要研究。广义霍夫变换（GHT）可有效地检测和识别轮廓对象作为道路标记。虽然GHT的精度在应用中不是很高。本文提出了一种基于边缘类型的广义霍夫变换（ETGHT）。边缘类型是通过提议的边缘特征的多个阈值划分获得的，并由多个R表记录。通过使用边缘点的位置和坡度方向的广度优先搜索策略来计算边缘特征。在应用中，提出了一种基于ETGHT的道路标记识别框架。首先，基于差分激励的边缘提取方法用于获得图像轮廓。然后提取输入图像的边缘点的边缘类型特征以确定相应的R表。在投票阶段，使用峰值区域筛选处理来提高系统的准确率。实验结果表明，该方法在保证查全率的同时，大大提高了查准率。

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