# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 4 Results
### Stage 5: Identification and Tracking  
The identification and monitoring stage of the developed model was organized in four main phases: Training, Validation, Testing, and Diagnosis. Each of these phases was designed to systematically evaluate the model's performance in classifying and tracking specific mining equipment, optimizing its predictive capability and ensuring the robustness of the predictions. In the training phase, a specifically designed dataset was used to train and adjust the model parameters. Through data augmentation and convolutional transformation techniques, the model learned to identify distinctive patterns of the target classes, including truck, excavator, operator, driller, and scoop. Figure 8 shows the training pipeline, while Figure 9 details the configurations used. The validation dataset was used to impartially evaluate the model fit during hyperparameter optimization. This phase allowed the performance of the model to be measured while avoiding overfitting. The results obtained, reflected in Figure 9, show robust performance with average accuracies of 98% in mining equipment classification. As metrics from the validation set were incorporated into the model fit, effective generalization to previously unobserved data was ensured. The testing phase provided an unbiased evaluation of the final model using a separate data set from training and validation. The results obtained, presented in Figure 9c, show that the model achieved an average accuracy of 95%. This performance confirms the model's ability to reliably classify mining equipment under test conditions. Consequently, it was evaluated whether the model meets the established accuracy requirements. Key results include: Accuracy rate: 100% rates were achieved for the classes \"excavator\", \"operator\" and \"drill\" in the validation stage, as well as 100% for \"excavator\" and \"drill\" in the test stage (See Figure 10). Loss function: Figure 11 shows the progressive drop of the loss function in the training set, indicating an effective optimization of the model. These results validate the robustness of the model and ensure its ability to generalize to new data. The results obtained in the metrics presented in Figure 10 show that the system achieves average classification accuracies of 98% in the validation stage and 95% in the test stage. These values support the acceptance of the alternative hypothesis (H1\({}_{1}\)) and reject the null hypothesis, since the average accuracies consistently exceed the 95% threshold. The consistency of the loss curves in Figure 11 reinforces this finding, evidencing that the model is adequately optimized and generalizes robustly on the test data. Figure 8: Close-up of an image with objects delimited and labelled by class. Figure 9: Set of augmented image processing for network training use. (**a**) Training, (**b**) Validation, (**c**) Test. According to the results reported in Figure 10, the \"digger\" and \"driller\" classes achieve 100% accuracy in the validation and test stages. This confirms the validity of the alternative hypothesis (H2\({}_{1}\)), as there are classes that achieve perfect accuracy in both stages. The quality of the model fit is reflected in the loss metrics (See Figure 11), where the sustained drop and final stabilization of the values reinforce the high accuracy observed in the specific classes. As an example of the application of these trainings, Figure 12a shows the system detecting an extraction process composed of different trucks, shovels, and vans in which the different types of equipment are labelled, which, in addition to identifying them, keeps a record of the quantity of each one, which is useful for further analysis. For the security case, Figure 12b shows a virtual demarcation of a zone, which can be regulated in size and shape and define different actions, such as generating an alarm when it is invaded.