This study systematically evaluates the performance of 15 conventional global single-threshold segmentation algorithms and three representative convolutional neural network (CNN) models across multi-modal geotechnical material images, including computed tomography (CT) and scanning electron microscopy (SEM) data. Based on their characteristics, thresholding methods are classified into three categories: histogram-based, entropy-based, and other approaches. Four types of geotechnical material CT images and two types of SEM images were selected as the evaluation datasets, and an objective assessment criterion that does not require manual annotation was proposed. The results indicate that the performance of different thresholding algorithms varies considerably across imaging modalities: the Otsu method performs best on coal and sandstone CT images, the Liu–S method (implemented in the JHNY-DPM software) excels on sandy soil CT images, and the Yen method demonstrates strong robustness on SEM images. However, all thresholding methods fail to effectively segment granite images with uneven grayscale distributions. In contrast, deep learning models exhibit superior performance across all modalities, with U-Net achieving the highest accuracy and stability during both training and validation without noticeable overfitting, significantly outperforming Fcn and Deeplabv3. Further experiments on a combined CT–SEM dataset reveal that despite domain adaptation challenges, U-Net can consistently segment complex geotechnical structures across different imaging modalities. Overall, the analysis demonstrates that deep learning models substantially enhance the accuracy and robustness of multi-modal geotechnical image segmentation, providing guidance for algorithm selection and supporting the unified processing of multi-source imaging data toward automation and intelligent analysis in digital geotechnical research.
Jian et al. (Sun,) studied this question.