一种基于IPM-DVS的车道线检测算法

An Algorithm of Lane Detection Based on IPM-DVS

为了提高在复杂情形下车道线检测的鲁棒性,提出了一种基于IPM-DVS的车道线检测算法。首先,对视频序列中连续两帧图像进行IPM得到鸟瞰图像,对这两帧鸟瞰图像进行差值运算,对差值图像进行Sobel算子卷积,然后将卷积结果与鸟瞰图像进行DVS,实现车道线信息的分离,最后根据车道线特征信息进行滤波检测。通过阴影、水渍、逆光等场景对该方法进行测试,实验结果表明:该方法能在各种复杂环境中检测出车道线,具有实时性好、鲁棒性强、正确率高的优点,适用于无人驾驶智能车视觉导航。

To improve the robustness of lane detection under complex conditions, proposed an algorithm of lane detection based on IPM-DVS. First, get the bird＇ s-eye images of the continuous two original images by IPM. Then, execute a Sobel operator convolution on the image of difference operation for the bird＇ s-eye images. After that, separate out the lane information from the original image via DVS of the result image of Sobel operator convolution and the IPM image. Finally, detect the lanes through the lane features filtering. The proposed algorithm is tested in several situations, including in the presence of shadow effects, waterlogging, and backlighting. The experimental results show that the method can detect the lane-marks in real time and robustness for various complex environments. The lane detection algorithm can be applied to the vision navigation for unmanned intelligent vehicles.