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

## TL;DR

This paper introduces a smartphone-based and AI-driven method for efficiently and accurately identifying rock joints in underground mines, replacing traditional manual methods.

## Contribution

The novel RC-Unet model integrates ResNet and CBAM for joint segmentation, enabling rapid and low-cost joint attitude calculation in underground mining environments.

## Key findings

- The RC-Unet model achieves accurate joint segmentation in underground mine images.
- PCP three-point localization algorithm efficiently calculates joint attitudes from segmented data.
- The method proves effective in simple underground environments as a replacement for manual cataloging.

## 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.