中文摘要：

裂缝作为土木工程中常见的病害，随着基于机器视觉检测设备的不断优化，如何对裂缝进行高效的识别与统计是整个系统高效运行的关键。然而这两者均涉及的一点就是特征选取。初选定特征后，如何对特征进行合理的分配应用是个需要探讨的问题。本文围绕裂缝特征及其应用展开，在选定裂缝特征之后，首先对应用部分裂缝特征的有效性进行了验证，即通过对处理得到的两值图像进行初筛选滤去大部分杂质；其后根据RBF-SVM算法建立自动判别模型，分别将6个特征（全部）和3个特征（部分）作为输入参数的工况进行结果对比，表明该模型具有很好的适应性，且均能高效实现裂缝识别，进而验证了裂缝特征分配应用的必要性。

英文摘要：

As the common defects, cracks are critical to the performance of civil engineering structures. With the continuous optimization of inspection system based on machine vision, the effective crack recognition and statistics are developed and become the key of the whole system, in which selection of characteristics is involved. After preliminary characteristics selection, the reasonable distribution and utilization of the characteristics should be further studied. After selecting the crack characteristics, the validity of crack characteristics application is verified by filtering a large number of impurities from the processed binary images. And then, an automated identification model is established according to the RBF-SVM algorithm. 6 characteristics （all） and 3 characteristics （part） are used as the input parameters, respectively. It shows that this model has good adaptability and high-efficiency in crack identification, which further validates the necessity of the distribution and utilization of crack characteristics.