在路面裂缝的检测中, 传统人工检测的方法准确性低, 效率低, 且单目视觉技术无法准确获得深度信息, 只能得到二维平面信息。基于此, 文章引入了双目线结构光技术, 提出了一种基于地面平均高度的三维裂缝提取算法, 实现了裂缝的三维信息采集。算法提取图像中每列像素值中最低点为起点, 向左右扩散形成一个边缘提取范围, 计算该列边缘提取范围以外的像素平均值作为地面平均高度值, 然后以最低点为中心开始遍历每个像素值, 当左右两边像素超过地面平均高度值时, 则确定两个像素点为裂缝边缘。整合所有列, 实现整条裂缝边缘的提取。实验结果表明: 文章提出的裂缝识别算法能够较为准确的对裂缝边缘进行提取, 通过与Canny边缘提取算法对比, 数据表明, 文章算法在连续性上表现更好, 本文算法的图像水平相关性数据总体都高于Canny边缘提取算法, 平均提高了30%。

In the detection of pavement cracks, the traditional manual detection method is low in accuracy and efficiency, and the monocular vision technology can not accurately obtain the depth information, only two-dimensional plane information. Based on this, binocular line structured light technology is introduced in this paper, and a 3D crack extraction algorithm based on the average ground height is proposed to realize the 3D crack information acquisition. The algorithm extracts the lowest point of each column of pixel values as the starting point, diffuses to the left and right to form an edge extraction range, calculates the average value of pixels outside the extraction range of the column edge as the ground average height value, and then starts to traverse each pixel value with the lowest point as the center. When the left and right pixels exceed the ground average height value, two pixels are determined as crack edges. All columns are integrated to achieve the extraction of the entire crack edge. The experimental results show that the proposed crack identification algorithm can extract the crack edge more accurately. Compared with the Canny edge extraction algorithm, the data show that the algorithm in this paper performs better in continuity. The horizontal correlation data of the algorithm in this paper are generally higher than that of the Canny edge extraction algorithm, with an average increase of 30%.