Northeast Petroleum University

Limestone thin-section classification now runs on a compressed MobileNetV3 backbone that folds texture enhancement and multi-level feature fusion into one lightweight network. Fine-grained carbonate grain boundaries and micritic matrix were previously attenuated by successive downsampling, while inter-class similarity stayed high and manual petrographic reading depended on expert judgment. On 3,600 images of six classes collected in Ordos, Xinjiang, Tibet and the Lesser Khingan Mountains, the method reaches 91.11% accuracy with macro precision, recall and F1 of 91.28%, 91.11% and 91.12%, at 2.095M parameters.

Limestone thin-section classification now runs on a compressed MobileNetV3 backbone that folds texture enhancement and multi-level feature fusion into one lightweight network. Fine-grained carbonate grain boundaries and micritic matrix were previously attenuated by successive downsampling, while inter-class similarity stayed high and manual petrographic reading depended on expert judgment. On 3,600 images of six classes collected in Ordos, Xinjiang, Tibet and the Lesser Khingan Mountains, the method reaches 91.11% accuracy with macro precision, recall and F1 of 91.28%, 91.11% and 91.12%, at 2.095M parameters.