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Lane Recognition Algorithm Using the Hough Transform Based on Complicated Conditions

机译:基于复杂条件的霍夫变换车道识别算法

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At present, most lane line detection methods are aimed at simple road surface. There is still no good solution for the situation that the lane line contains arrow, text and other signs. The edge left by markers such as arrow and text will interfere with the detection of lane lines. In view of the situation of arrow mark and text mark interference between lane lines, the paper proposes a new processing algorithm. The algorithm consists of four parts, Gaussian blur, image graying processing, DLD-threshold (Dark-Light-Dark-threshold) algorithm, correlation filter edge extraction and Hough transform. Among them, the DLD-threshold algorithm and related filters are mainly used to remove the identification interference between lane lines. The test results on the Caltech Lanes dataset are given at the end of the article. The result of verification of this algorithm showed a max recognition rate of 97.2%.
机译:目前,大多数车道线检测方法都针对简单的路面。对于车道线包含箭头,文字和其他标志的情况,仍然没有好的解决方案。箭头和文字等标记留下的边缘会干扰车道线的检测。鉴于车道线之间箭头标记和文字标记的干扰情况,提出了一种新的处理算法。该算法由四部分组成:高斯模糊,图像灰度处理,DLD阈值(Dark-Light-Dark-threshold)算法,相关滤波器边缘提取和Hough变换。其中,DLD阈值算法和相关滤波器主要用于消除车道线之间的识别干扰。文章结尾给出了Caltech Lanes数据集的测试结果。该算法的验证结果显示最大识别率为97.2%。

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