An improved automated testing model for maceral groups in coals based on DeeplabV3+

The identification of maceral groups in coals plays a critical role in analyzing the chemical properties of coals. However, manual identification is laborious and requires high expertise. Existing computer-assisted identification methods, mostly adopting deep learning-based semantic segmentation models, fail to accurately identify maceral groups in coals due to complex compositions of microscopic coal images and the presence of transitional components. Therefore, this study proposed an improved DeeplabV3+ semantic segmentation model integrating the Swin Transformer backbone network and the SkNet. First, to deal with the challenge of feature extraction caused by the intertwined maceral groups and the presence of transitional components in microscopic coal images, the Swin Transformer backbone network was used as the basic feature extraction network to enhance the feature extraction ability of the model for various maceral groups and to enable the information interaction between features of the segmentation network. Second, to improve the feature utilization rate of the Atrous Spatial Pyramid Pooling (ASPP) module in the model, the SkNet network was integrated into the ASPP to enable the ASPP to extract important features and suppress unnecessary features that interfere with the final prediction results. Finally, the improved DeeplabV3+ model was compared with existing advanced algorithms through experiments. As indicated by the comparison results, the improved model yielded pixel accuracy of 92.06% on the test set of microscopic coal images, which was 9.48%, 6.90%, and 3.40% higher than that of the random forest method, the U-Net semantic segmentation model, and the DeeplabV3+ semantic segmentation model, respectively. Furthermore, the improved model showed results similar to the manual point measurement method. Therefore, the improved model, outperforming the existing automatic identification models for coal maceral groups, can serve as a powerful method for the computer-assisted manual identification of maceral groups in coals.