为了更加准确高效地识别路面裂缝，提出一种基于路面 3D 数据和双尺度聚类算法的路面裂缝检测技术． 首先，使用中值滤波以及 Otsu 阈值分割算法对采集到的路面裂缝 3D 数据进行预处理，获得二值化的路面裂缝图像，在此基础上，将不规则的路面裂缝区域使用规则椭圆模型表征为裂缝基本单元，作为聚类基础． 然后，使用双尺度准则函数从最优化中心距离以及角度偏差两个尺度上对基本裂缝单元进行聚类识别． 最后借助杠杆原理确定聚类后裂缝中心点位置，使用最小外接椭圆表征聚类后的实际裂缝区域，并使用圆形度因子进行了路面裂缝类型判断，对网状裂缝进行了路面破损程度参数分析，为路面破损程度量化分析提供了参考因子． 实际路面裂缝的实验表明，文中方法具有良好的路面裂缝检测精度．

In order to identify the pavement cracks more accurately and efficiently，a kind of two-scale clustering pavement crack recognition method is proposed based on 3D data． First，the 3D data of pavement cracks are pretreated by means of the median filtering and the Otsu threshold segmentation algorithm，and the binary pavement crack images are thus obtained． Next，the irregular pavement crack area is characterized by a regular elliptical crack model as the basic unit，which represents a cluster basis． Then，the two-scale optimization criterion function is used to identify the basic units from the viewpoints of the distance and the angle deviation． Finally，the center of the final position of the cluster is determined based on the lever principle，and the area of the clustered cracks is characterized by using the minimum external ellipse of a complete crack． Moreover，the degree of circularity factors are adopted to judge the types of pavement cracks，and the parameters relevant to the damage extent of the road with reticular cracks are analyzed，which provides reference factors for the quantitative analysis of the road damage extent． The experimental results of the actual pavement cracks show that the proposed method is of a high degree of accuracy in detecting pavement cracks．