## 4 Results and Discussion
### Intelligent Detection of UAV Images
#### 4.2.2 Magnetite Identification
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predictive Models** & **Recall** & **Precision** & **F1 Score** \\ \hline SVM12 & 0.75 & 0.78 & 0.76 \\ RF23 & 0.80 & 0.79 & 0.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Optimal model accuracy, recall, and \(F1\) score of SVM12 and RF23. Subsequently, the RF23 model was applied to predict the entire region of a multispectral image, with the results shown in Figure 16. The prediction results indicate a clear boundary between the high and low mineralization areas. In Figure 16a, regions 1 and 2 represent high mineralization areas, while regions 3 and 4 correspond to low mineralization areas. The distribution of samples aligns well with the predicted two-dimensional magnetite mineralization levels, further confirming the reliability of this method for intelligent recognition of two-dimensional magnetite. At the same time, we zoomed in on the magnetite identification results in the local ore stockpile area, allowing for a clearer  
Figure 14: AUC values from thirty rounds of training using Support Vector Machine (**a**) and Random Forest (**c**) models, along with the confusion matrix for SVM12 (**d**) and RF23 (**b**). Figure 15: Optimal ROC curves of SVM12 and RF23. and more intuitive view of the distinct boundaries between rocks and minerals. From the comparison results of Figure 16b,c, it can be observed that the red-boxed area on the right side of Figure 16b represents the rock powder stockpile area. The high-grade region without white cloth coverage and the low-grade region covered with white cloth in the stockpile are both clearly highlighted. The red-boxed area on the left side of Figure 16b represents the face of the open-pit excavation area, where the boundary between rock and ore cannot be effectively distinguished in the visible light angle of the DOM image (Figure 16c). However, in the multispectral image's intelligent interpretation and recognition results, the rock-ore boundary is very clear and can effectively guide real-time mining operations in the area. The raster image of intelligent recognition of magnetite in 2D form is converted into point data in ArcGIS_Pro 3.4. Then, the surface elevation information obtained during LiDAR imaging is appended to it, forming a three-dimensional surface magnetite intelligent recognition result with elevation attributes, as shown in Figure 17. The recognition model of this technology not only displays the magnetite prediction results for the entire mining area, as shown in Figure 17, where the overall iron grade of the mining area is relatively low, and the iron grade in the central and western mining areas is slightly higher, but it can also significantly compress the task completion time by hours through the instantaneous availability of data. This enables dynamic risk control and resource optimization, such as real-time mining guidance and monitoring of slope stability, reducing the cost and risk of traditional manual exploration and improving production efficiency and safety. Additionally, this technology can not only quickly interpret and identify mining areas but also detect the diffusion of heavy metal pollution (such as arsenic and mercury) in tailings using spectral features and assess the ecological restoration effect using NDVI. Figure 16: Magnetic identification results (**(a)** identification results for the whole region (Regions 1 and 2 are high mineralization areas, 3 and 4 are low mineralization areas, and 5 are enlarged interception areas); (**b**) local recognition result; (**c**) local DOM image corresponding location).