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Research on Lane Line Detection Method Based on Improved Hough Transform

机译：基于改进霍夫变换的车道线检测方法研究

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Lane line is an important reference for safe driving. In order to improve the accuracy and real-time performance of lane line detection, a lane line detection algorithm based on improved Hough transform is proposed in this paper. Firstly, the lifting algorithm of wavelet is used to extract the low-frequency wavelet coefficients of the image, so as to reduce the complexity of the image and improve the efficiency of image processing; Then Canny operator is used to detect the edge of the image region of interest, and threshold is automatically selected according to edge information for threshold processing; Finally, three constraints are proposed from two aspects of angle and lane width to improve the Hough transform to detect lane lines, and the correct lane lines are fitted by linear regression method. Experiments show that the proposed algorithm has good correctness and real-time performance for lane line detection. The recognition accuracy is above 94% and the average processing time of each frame is 25.6ms.
机译：车道线是安全驾驶的重要参考。为了提高车道线检测的准确性和实时性，提出了一种基于改进的霍夫变换的车道线检测算法。首先，利用小波提升算法提取图像的低频小波系数，以降低图像的复杂度，提高图像处理效率。然后用Canny算子检测感兴趣图像区域的边缘，并根据边缘信息自动选择阈值进行阈值处理。最后，从角度和车道宽度两个方面提出了三个约束条件，以改进霍夫变换来检测车道线，并通过线性回归方法拟合正确的车道线。实验表明，该算法在车道线检测中具有良好的正确性和实时性。识别精度高于94％，每帧的平均处理时间为25.6ms。

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