Intelligent assessment of surrounding rock grade of tunnel face based on multi-scale geological feature enhancement and deep convolutional network

To address the issues of image quality degradation and single feature expression in tunnel face images, an intelligent rock mass evaluation method integrating multi-scale progressive enhancement and deep semantic modeling was proposed. A multi-scale image enhancement framework combining spatial domain filtering and transform domain denoising was employed, by which the signal-to-noise ratio was increased by 12.7 dB with significantly enhanced visibility of key geological structures including fractures and joints. A four-dimensional comprehensive evaluation index system was constructed based on the gray-level co-occurrence matrix, improved local binary pattern, and RGB color moments for quantitative rock mass characterization.The ResNet architecture was improved through integration of multi-scale feature extraction and dual attention mechanisms (channel attention + spatial attention), while a weighted cross-entropy-label smoothing composite loss function was adopted to address class imbalance. Based on a constructed database of 5,000 tunnel face images, the model accuracy of 94.27% was achieved on the test set (4.93% higher than the baseline model) with a computational complexity of 4.8 G FLOPs. Practical case verification indicated that the proposed method could provide real-time, objective geological decision support for tunnel construction, significantly improving the intelligence level of rock mass classification.