Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Sensors

DOI: 10.3390/s25206410

Authors: Guang Li, Jinyao Zhu, Changyu Jin, Xinyang Mao, Qiang Wang

Institution: Northeastern University

Language: English

Topic: Rock Mechanics and Modeling

Subfield: Mechanics of Materials

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: National Natural Science Foundation of China, Higher Education Discipline Innovation Project, National Key Research and Development Program of China