Intelligent rock classification from a mesoscopic perspective: A computer vision approach based on thin section images

This publication has 42 references indexed in Scilit:

• Att-YOLO: A Real-Time Rock Core Classification and Localization Deep Learning Model
  Rock Mechanics and Rock Engineering, 2025
• Unsupervised Machine Learning for Data-Driven Rock Mass Classification: Addressing Limitations in Existing Systems Using Drilling Data
  Rock Mechanics and Rock Engineering, 2024
• AI-Powered Geotechnics: Enhancing Rock Mass Classification for Safer Engineering Practices
  Rock Mechanics and Rock Engineering, 2024
• Assessment of the Relationship Between Rock Mass Classification Systems Using Hybrid XGBoost Algorithms: Insights from the Mechanized Tunnelling Project Database
  Rock Mechanics and Rock Engineering, 2024
• A Causal Analysis of the Influential Criteria in Underground Mining Method Selection
  Rock Mechanics and Rock Engineering, 2024
• Rock thin section image classification based on depth residuals shrinkage network and attention mechanism
  Earth Science Informatics, 2023
• Novel Rock Image Classification: The Proposal and Implementation of HKUDES_Net
  Rock Mechanics and Rock Engineering, 2023
• Evaluation of machine learning methods for rock mass classification
  Neural Computing and Applications, 2021
• Rock classification in petrographic thin section images based on concatenated convolutional neural networks
  Earth Science Informatics, 2020
• A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength
  Neural Computing and Applications, 2020