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Lane-line Detection Algorithm for Complex Road Based on OpenCV

机译:基于OpenCV的复杂道路车道线检测算法

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摘要

In order to meet the requirements of the high accuracy and timeliness of lane line detection for the autonomous vehicle camera, a fast detection algorithm based on the combined gradient and color filtering of lane line pixel for the interest model was proposed. Firstly, based on the high contrast between the lane line and the surface of the structured road, the algorithm uses Sobel edge detection operator to detect the edge information of the lane. Then, according to the white and yellow color features of the lane line, the pixel of the lane line is extracted by filtering these two colors in the color space. Finally, a relatively stable lane line extraction method is obtained based on combining the edge gradient and color filter in the region of interest model.Experiments show that the algorithm has high accuracy, fast speed and good robustness, and can meet the requirements of lane line detection under complex road conditions.
机译:为了满足自动驾驶汽车摄像机车道线检测的高精度和及时性的要求,提出了一种基于兴趣线模型的车道线像素梯度与滤色相结合的快速检测算法。首先,基于车道线与结构化道路表面之间的高对比度,该算法使用Sobel边缘检测算子来检测车道的边缘信息。然后,根据车道线的白色和黄色颜色特征,通过在颜色空间中对这两种颜色进行过滤来提取车道线的像素。最后,在目标区域模型中结合了边缘梯度和滤色器,获得了一种相对稳定的车道线提取方法,实验表明该算法具有较高的精度,速度和鲁棒性,可以满足车道线的要求。在复杂的道路条件下进行检测。

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