Beamlet变换是一种多尺度分析的有效工具。对基于Beamlet变换的线特征提取算法进行改进，提出一种表示Beamlet上图像灰度值加权平均的算式，提出在图像子块内沿Beamlet的各个方向搜索边缘，形成一种图像边缘检测的新算法。从检测到的边缘连贯性等方面对该算法的性能进行了评价，将该算法应用于车道线等图像的边缘检测和车道识别。实验结果表明，该算法检测到的边缘连贯性好，算法的错检率和漏检率低，且具有较强的提取线特征的能力；检测到的边缘线段包含位置、方向等信息，便于对车道等目标进行识别；算法的缺点是：抗噪性不够好且计算较为复杂，有待改进。

The Beamlet transform is an efficient tool for multi-scale analysis. A new edge detection algorithm is proposed through improving the algorithm of linear feature extraction based on Beamlet transform. A definition of weighted average for the gray values on a Beamlet is proposed, all dyadic squares of an image are searched for edge in all directions of Beamlets. The evaluation of this algorithm is taken from several aspects, such as the continuity of the edge detection, the antinoise performance and so on. The algorithm is applied to detect edge and identify lane from road images. The experiments show that the continuity of edge detection is good, the new method has low rate of wrong detection and miss detection, which also has good performance on line extraction. But the performance of antinoise is inferior and the calculation has low speed. The edge we gained contains information on the position, the orientation and so on, which will be convenient for the target recognition.