Lane Detection Algorithm Based on Haar Feature Based Coupled Cascade Classifier

Hongyu Zhou, Xuanzhang Song
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引用次数: 1

Abstract

To improve the accuracy of lane detection in complex environment, The lane detection algorithm based on Haar feature coupled cascade classifier was proposed. The input image was scaled, and the Regions of Interest (ROI) was extracted according to the position of the vanishing line. The Haar feature of the lane was extracted from the ROI, and a cascade Lane classifier was introduced to roughly detect the lane in the ROI. The line segment detector (LSD) method was used to fit roughly detected lanes. The growth strategy and geometric checking were combined to optimize fitting results and complete target detection. The tests were conducted on multiple datasets, and results show compared with current lane detection methods, the proposed algorithm has higher robustness and accuracy, which was up to 96.5%.
基于Haar特征耦合级联分类器的车道检测算法
为了提高复杂环境下车道检测的精度,提出了基于Haar特征耦合级联分类器的车道检测算法。对输入图像进行缩放,根据消失线的位置提取感兴趣区域(ROI)。从感兴趣区域中提取车道的哈尔特征,引入级联车道分类器对感兴趣区域中的车道进行粗略检测。采用线段检测器(LSD)方法对检测到的车道进行粗略拟合。结合生长策略和几何检查优化拟合结果,完成目标检测。在多个数据集上进行了测试,结果表明,与现有车道检测方法相比,该算法具有更高的鲁棒性和准确率,准确率可达96.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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