## 2 Methodology
### Calculation Method of Rock Mass Evaluation Indices  
First, laboratory experiments, such as direct shear experiments [18], uniaxial compression experiments [19], and Brazilian splitting experiments [20], are conducted to obtain the physical and mechanical parameters of the rock, including uniaxial compressive strength, cohesion, etc. Then, structural surface parameters, such as joint orientations, joint combinations, joint spacing, joint density, and the number of joints, are acquired using digital photogrammetry techniques. More details on these parameters are provided in Section 4. These data provide the foundation for subsequent rock mass quality classification and the determination of refined mechanical parameters for the rock mass. Rock quality designation (RQD) is one of the widely used methods for classifying engineering rock masses. It is also used to calculate the Geological Strength Index (GSI). However, traditional manual logging methods for RQD in drilling cores are inefficient and prone to subjective bias. This paper introduces an intelligent RQD recognition method based on the mask region-based convolutional neural network (Mask R-CNN) deep learning model [21], which enhances data processing efficiency and allows for refined rock mass quality characterization in mining projects. Mask R-CNN is an improvement on the Faster R-CNN model [22] and is classified as a two-stage detector. The basic structure of the Mask R-CNN instance segmentation model includes the backbone structure, feature pyramid networks (FPN), region proposal network (RPN),  
Figure 2: Construction process flowchart of the 3D refined computational entity model. region of interest align (RoI Align), and output components (bounding box for target location, predicted target class, and mask based on the bounding box), as shown in Figure 3.  
The backbone structure of Mask R-CNN is a standard convolutional neural network (CNN) designed to extract information from images. In addition to this, Mask R-CNN employs a feature pyramid network (FPN) to improve the network's performance in extracting features at different scales. For large objects, deep features are used, while shallow features are applied for smaller objects. The region proposal network (RPN) is primarily responsible for generating object detection boxes. Its core operation involves sliding windows across feature maps to generate a series of anchor boxes, according to specific rules. Once these anchor boxes are obtained, the RPN determines whether each anchor box contains an object of interest and then adjusts the anchor box to better align with the actual object. The RPN classifies and regresses the bounding boxes, extracting high-scoring regions of interest (ROIs) and applying non-maximum suppression (NMS) [23]. These ROIs are then input into the ROI align layer for feature extraction. From the architecture of Mask R-CNN, it can be observed that the ROI layer has two inputs. One is the feature map generated by the backbone structure, and the other is the detection box generated by the RPN. The primary responsibility of the ROI layer is to map the detection boxes to the corresponding regions on the feature map. In the ROI align layer, the selected ROIs from the RPN are used to extract features from the full image feature map generated by the backbone structure. Nonlinear interpolation is employed to obtain values at floating-point positions, producing a fixed-size feature map for each ROI. The mask branch and box regression operate simultaneously in the Mask R-CNN architecture. The mask branch processes each region of interest (ROI) and generates a pixel-level segmentation mask for each candidate region. The segmentation mask for each ROI is predicted pixel-by-pixel, according to the category of the object, producing a binary mask that marks the pixel areas belonging to that object. Meanwhile, box regression optimizes the bounding boxes further through fully connected layers to obtain more precise detection box locations.