# Real-time grading method of tunnel surrounding rock based on image recognition

## TL;DR

This paper introduces a real-time method using image recognition and deep learning to quickly and accurately grade tunnel surrounding rock during construction.

## Contribution

A novel real-time grading method combining image processing and lightweight deep learning for tunnel surrounding rock.

## Key findings

- The proposed method achieved 85% accuracy in lithology classification.
- Rock-mass integrity classification reached 75% accuracy in field tests.
- The overall surrounding-rock grading accuracy was 80% on an engineering site.

## Abstract

To enable rapid, accurate grading of tunnel surrounding rock during construction, we propose a real-time grading method that integrates image processing with lightweight deep learning. We developed an automated pipeline that combines image-processing techniques and machine-learning algorithms to extract and classify characteristic parameters of tunnel surrounding rock, enabling real-time monitoring and classification at the tunnel palm surface. The study demonstrates that: (1) Following the proposed image-acquisition standards for rock and tunnel palm surfaces, images are converted to grayscale, denoised, enhanced, and normalized, which facilitates efficient and accurate extraction of structural features and improves the precision of classification parameters; (2) An optimized lithology identification and classification model was built, and a rock-hardness, strength, and integrity sensing approach based on the ShuffleNetV2 convolutional neural network was introduced to achieve real-time surrounding-rock grading. On an engineering site, the method attains 85% accuracy for lithology classification, 75% for rock-mass integrity, and 80% for overall surrounding-rock grade, confirming its feasibility and practical value. These results offer theoretical insight and engineering utility for the scientific evaluation of tunnel surrounding-rock grade.