Pavement crack plays an important role in estimating pavement conditions and implementing pavement maintenance management. There are three shortcomings summarized. (1) Low contrast between crack pixels with surrounding pixels; (2) intensity values along crack discontinuous; (3) complexity structure instead of unitary and unbranched cracks. The proposed algorithm, including three essential steps to address the above disadvantages. A fast nonlocal means denoising method (FNLM) is first proposed to eliminate Gaussian isolated noise while retains target pixels. Second, the basic filter named steerable filter (SF), which calculates the maximal response with different orientations at a certain position, is made up of the second partial derivatives of a two-dimensional Gaussian function. Then the crack saliency map is obviously generated. Last, the local order energy (LOF) is applied to extract crack features from the saliency map to generation binary image, and the mathematical morphology operation method is utilized to locate the location and orientation of crack. Different types of pavement images are applied to estimate the performance of the proposed algorithm, and the experimental results represent the proposed method can achieve fast and robust performance.

路面裂缝在估算路面状况和实施路面维护管理中起着重要作用。总结了三个缺点。（1）裂纹像素与周围像素之间的对比度较低；（2）沿裂纹的强度值不连续；（3）复杂的结构而不是单一和无分支的裂纹。提出的算法包括解决上述缺点的三个基本步骤。首先提出一种快速的非局部均值去噪方法（FNLM），以消除高斯孤立噪声，同时保留目标像素。其次，基本滤波器称为可控滤波器（SF），它由二维高斯函数的第二阶导数组成，该滤波器可计算特定位置上不同方向的最大响应。然后，明显生成了裂纹显着性图。持续，利用局部阶次能量（LOF）从显着性图提取裂纹特征并生成二值图像，利用数学形态学运算方法确定裂纹的位置和方向。应用不同类型的路面图像来评估该算法的性能，实验结果表明该方法可以实现快速，鲁棒的性能。