一种改进的路面裂缝图像分割方法

针对公路裂缝检测中的图像分割问题，运用机器学习领域的方法，以裂缝图像为研究对象，在深入分析经典K-Means聚类算法以及高斯混合模型（Gaussian Mixture Model, GMM）之后，提出一个利用全局K-Means和高斯混合模型来对公路裂缝图像进行分割的方法。该方法在全局K-Means聚类算法的基础上，采用改进策略，同时，将传统GMM的每一个概率分布，进一步再细分成单个的概率成分，在辅助变量计算过程中，采用EM算法。仿真图像和实际裂缝图像实验结果显示，本研究得到了比使用传统K-Means算法和普通GMM的分割方法精度更高、稳定性更好的分割结果。

An Improved Road Crack Image Segmentation Method

Aiming at the image segmentation problem in highway crack detection, using the method of machine learning field, taking crack image as the research object, after analyzing the classic K-Means clustering algorithm and Gaussian Mixture Model (GMM), this paper proposes a method of segmenting highway crack images using global K-Means and Gaussian mixture models. Based on the clustering of global K-Means algorithm, this method adopts an improved strategy. At the same time, each probability distribution of traditional GMM is further subdivided into individual probability components. In the process of calculating auxiliary variables, EM algorithm is adopted. The experimental results of the simulated image and the actual crack image show that the proposed method has higher accuracy and better stability than the segmentation method using the traditional K-Means algorithm and the ordinary GMM.