Markov Random Field Road Crack Image Segmentation Algorithm Integrating Multi-Scale Features

In order to improve the accuracy and robustness of the pavement crack segmentation,a Markov random field model for crack segmentation that integrates gray-scale statistical models and multi-scale feature vectors is proposed.First,based on the histogram of the crack image,the Gaussian distribution and the Rayleigh distribution are used to frame the crack image gray-scale statistical model,and the EM algorithm is adopted to optimize the model parameters.Then,the Hessian matrix of the crack is constructed by the convolution of the bidirectional Gaussian kernel function and the crack image,and the multi-scale feature vector of the crack image is extracted by calculating the response value of the crack measurement at different scales to enhance the tree-like geometric structure.Finally,the multi-scale feature vectors are integrated into the Markov random field model of crack segmentation,and based on the minimum energy criterion,a conditional iterative algorithm is applied to solve the maximum label field of the crack.On the public data set CrackTree206 and 200 self-built crack data sets,it is compared with 3 algorithms such as MRF.The experimental results show that the probabilistic rand index reaches 91.78%,the global consistency error index reaches 9.86%,and the evaluation index is better than other algorithms,indicating that the algorithm can effectively improve the accuracy of crack segmentation.