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首页> 外文会议>International conference on graphic and image processing >A Lane Line Segmentation Algorithm Based on Adaptive Threshold and Connected Domain Theory
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A Lane Line Segmentation Algorithm Based on Adaptive Threshold and Connected Domain Theory

机译：基于自适应阈值和连通域理论的车道线分割算法

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Before detecting cracks and repairs on road lanes, it's necessary to eliminate the influence of lane lines on the recognition result in road lane images. Aiming at the problems caused by lane lines, an image segmentation algorithm based on adaptive threshold and connected domain is proposed. First, by analyzing features like grey level distribution and the illumination of the images, the algorithm uses Hough transform to divide the images into different sections and convert them into binary images separately. It then uses the connected domain theory to amend the outcome of segmentation, remove noises and fill the interior zone of lane lines. Experiments have proved that this method could eliminate the influence of illumination and lane line abrasion, removing noises thoroughly while maintaining high segmentation precision.
机译：在检测车道上的裂缝和维修之前，必须消除车道线对车道图像识别结果的影响。针对车道线引起的问题，提出了一种基于自适应阈值和连通域的图像分割算法。首先，通过分析灰度分布和图像照度等特征，该算法使用霍夫变换将图像划分为不同的部分，并将其分别转换为二进制图像。然后，它使用连通域理论来修正分割结果，消除噪声并填充车道线的内部区域。实验证明，该方法可以消除光照和车道线磨损的影响，在保持较高分割精度的同时，彻底消除噪声。

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