## 4 Experiments
### Quantitative Experimental Results  
As metrics broadly used for the performance evaluation of deep learning models, the mean intersection over union (mIoU), precision, recall, and similarity coefficient (Dice) are used in this paper as the evaluation indexes to assess the model's segmentation effect on the remote sensing image of the open-pit mining area, and they are calculated as shown in (22) to (25). Among them, as IoU is the ratio of the intersection and merger of real labels and predicted masks in a certain category, and mIoU is the average of intersection and merger ratios of all the categories, which measures the degree of overlap between the pixel categories predicted by the model and the real labels. Precision indicates the proportion of pixels predicted to be true that are actually true. Recall measures the ability of the model to recognize all positive categories, i.e., the ratio of the number of pixels predicted to be true to the total number of pixels that are actually true. Similarity coefficient (dice), an evaluation metric combining precision and recall, is used to compute the similarity between the true labels and the predicted masks and is expressed as the ratio of twice the intersection of the predicted and true labels to the sum of predicted masks plus true labels. \[mIoU=\frac{1}{k+1}{\sum_{i=0}^{k}\frac{TP}{FN+FP+TP}} \tag{22}\]  
\[Precision=\frac{TP}{TP+FP} \tag{23}\]  
\[Recall=\frac{TP}{TP+FN} \tag{24}\]  
\[Dice=\frac{2 TP}{FP+2 TP+FN} \tag{25}\]  
where, \(k\) denotes the total number of categories; true positive example (TP) denotes the number of pixels that are actually true and predicted to be true; false positive example (FP) denotes the number of pixels that are actually false and predicted to be true; true negative example (TN) denotes the number of pixels that are actually false and predicted to be false; and false negative example (FN) denotes the number of pixels that are actually true and predicted to be false. In order to verify that the proposed model is superior to the traditional models, six additional semantic segmentation models were selected, and a comparative experimentwas designed. During the experiment under the same environment, detailed comparative analyses were carried out from both the overall and local perspectives, respectively. Four evaluation metrics were chosen to evaluate the overall accuracy of each model, and seven scenes were selected to evaluate the local accuracy of each model. The experimental results are shown in Table 1, which compares the mIoU, precision, recall, and dice results of each network model, with the bolded data indicating the optimal results. As can be seen from Table 1, all seven Transformer-based models obtain better segmentation performance. This is due to the fact that Transformer obtains a sufficiently large sensory field through the multi-head self-attention mechanism, which enables the original fuzzy boundaries to be judged more accurately through its overall shape and contextual information. Both Segmenter and RtFormer retain the high-resolution, coarse-grained features and low-resolution, fine-grained features, which makes the pixel classification more accurate and the segmentation effect of the boundaries and other details finer. Among them, the mIoU of Segmenter reaches 85.68%, which is due to the fact that Segmenter utilizes the self-attention mechanism to capture the global context information and generate classification masks through attention weights, which, in turn, leads to more complete segmentation of open-pit mining areas. SegFormer, TopFormer, Mask2 Former, and ViT-Adapter all utilize an encode-decode structure, and all incorporate multi-scale semantic information. Utilizing features at different scales can improve the model's ability to recognize objects with different sizes and complex shapes. Among them, the mIoU of Mask2 Former reaches 86.12%, which is due to the fact that Mask2 Former transforms the segmentation task into a combined problem of category prediction and instance segmentation.