A Novel Method for the Intelligent Recognition of Lattice Fringes in Coal HRTEM Images Based on Semantic Segmentation...

High-resolution transmission electron microscopy (HRTEM) can directly obtain the lattice fringes and structure parameters of coal. Aiming at present problems in extracting lattice fringes in HRTEM images, such as unlocated fringe regions, single-threshold segmentation, unclassified fuzzy superpixels, and tedious fringe pruning, an intelligent recognition method based on semantic segmentation, deep neural networks, fuzzy superpixels, and other algorithms is proposed. For unlocated fringe regions, the fringe regions are automatically localized with semantic segmentation. The whole semantic segmentation network adopts DeepLab V3+ based on ResNet to reduce unnecessary operations brought by non-fringe regions. For single-threshold segmentation of the image, the image is chunked before anything else. The genetic-optimized watershed algorithm is applied to divide the fringe base maps and non-fringe ones in order to avoid the distortion caused by different lights and shades of the image. For the fuzzy superpixels between the fringes and non-fringes, a similarity category judgment method based on neighboring pixels is proposed to solve the problem of unclassified fuzzy superpixels and to enrich and perfect the information of the lattice fringe base map. Eventually, as for lattice fringe overlap caused by coals piling together, a similarity judgment method based on the fringes' characteristics is proposed to remove the bur portion of the lattice fringes and improve the pruning rate. Combining the above theories, a visualization tool based on MATLAB App Designer is designed, and the above four steps can be completed by this app to accurately display the results of coal aromatic lattice fringe identification in HRTEM images. Comparison with the lattice fringes drawn by leading experts shows that the fringes interpreted by this method are reliable. This method facilitates the extraction of lattice fringes in HRTEM, which lays the foundation for the labeling of HRTEM images in a variety of deep learning algorithms and facilitates the direct observation of coal structures by researchers.