An Intelligent Joint Identification Method and Calculation of Joint Attitudes in Underground Mines Based on Smartphone Image Acquisition

Acquisition of joint attitudes is vital in mine geology but often constrained by underground conditions, while manual cataloging remains inefficient and subjective. To overcome these issues, we propose a mobile phone photography and deep learning-based method. Rock joint images are collected with smartphones, augmented by cutting and rotation, and enhanced using CLAHE. After labeling with Labelme, a dataset is built for training. A ResNet residual module and CBAM attention are integrated into a U-Net architecture, forming the RC-Unet model for accurate semantic segmentation of joints. Post-processing with OpenCV enables contour extraction, and the PCP three-point localization algorithm rapidly calculates joint attitudes. A practical engineering case verifies that intelligent joint identification can replace manual cataloging in relatively simple underground environments. This approach improves efficiency, reduces subjectivity, and provides a rapid, low-cost, and easily storable means for geological information acquisition, highlighting its potential as an effective tool and supplementary method for mine surveys.