The work aims to propose a glass defect segmentation method based on a dual feature Gaussian mixture model to solve the low accuracy of traditional glass defect segmentation algorithm caused by inherent properties of glass material, such as transparency and stripe noise. Firstly, fractional calculation and gray-level co-occurrence matrix were adopted to enhance glass defects, and obtain texture features, respectively, and thereby construct dual feature vectors of glass defects. The dual feature vector was introduced into the Gaussian mixture model, and the adjacent pixel spatial information of the Markov random field was used to improve the glass defect segmentation Gaussian mixture model. Then, the glass defect segmentation was completed by alternately performing the estimation of the mapping relationship between the glass defect pixels and the label field and the updating based on the space constraint of the Gaussian kernel function. Finally, fuzzy entropy was applied to the subsequent processing of the defect image segmentation results. The performance test and comparative analysis experiment of different algorithms were performed on four typical defect sample images of furuncle, stain, bubble and inclusion. The experimental results showed that the Dice index of the proposed algorithm reached 98.59% and the Mcr index reached 7.03%, which was better than that of other algorithms. Introducing gray-level features and texture features into the Markov random field of glass defect segmentation can suppress non-defect targets, retain low-contrast glass defects, and improve the robustness and accuracy of the glass defect segmentation algorithm.