# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 4 Results
### Stage 5: Identification and Tracking
The evaluation of classification models in machine learning depends on key metrics such as precision and recall, which capture different aspects of predictive performance. While precision evaluates the accuracy of positive predictions, recall measures the model's ability to correctly identify actual positive cases. Analyzing these metrics together allows us to understand the trade-off between minimizing false positives and false negatives. Given the above, hypothesis H3 is corroborated. By means of a dynamic heat map, it is possible to analyze how the precision and recall metrics evolve over time (see Figure 13). Figure 11: Loss function result curves for network training. Figure 10: Result of test set and validation. The results show the temporal evolution of precision and recall metrics over 100 epochs, highlighting the different trends between these performance indicators. Initially, precision shows considerable variability, especially between epochs 1 to 10, before stabilizing at higher values (\(\sim\)0.75-0.8) in subsequent epochs. Recovery, on the other hand, shows a gradual upward trajectory, stabilizing in the later epochs, albeit with small fluctuations at specific intervals. This stabilization towards the later epochs provides empirical support for the H3\({}_{1}\) hypothesis, stating that the metrics show significant stabilization patterns, especially within the majority classes. However, the analysis also reveals nuanced inconsistencies, especially during transitional phases (e.g., epochs 20-40 and 70-80), suggesting differential convergence behaviors between majority and minority classes. These results corroborate the premise of the hypothesis H3\({}_{1}\), with implications for class-optimization strategies and dynamic adjustment mechanisms in model training to improve cross-class consistency in performance metrics. In the field of deep learning applied to object detection tasks, evaluation metrics play a crucial role in measuring the performance of trained models. Two fundamental metrics used are the mAP50 and the mAP50-95. The mAP50 (Mean Average Precision at an IoU threshold \(\geq\) 0.5) evaluates the average accuracy of the model in correctly identifying objects in different categories, considering a minimum overlap of 50% between predictions and actual labels. On the other hand, the mAP50-95 extends this analysis by calculating the average accuracy at multiple IoU thresholds, from 0.5 to 0.95, offering a more comprehensive comparison of the mAP50-95. Figure 12: Identification of mining equipment and delineation of safety zones. (**a**) Identification of mining equipment; (**b**) delineation of safety zones. Figure 13: Heat graph of the evolution of precision. sive and stringent view of the model's performance. These metrics not only allow us to quantify the quality of the predictions but also facilitate the analysis of the model's behavior throughout the training process. In this context, hypothesis H4 is tested to determine whether the metrics show an increasing trend towards convergence, indicating that the model reaches its optimal performance before completing all training epochs. The heat plot shows the evolution of the mAP50 and mAP50-95 metrics over 100 training epochs, with values progressively increasing from 0.1 to approximately 0.7 (see Figure 14). A convergence trend is observed in both metrics, with a reduction in the performance gap as epochs progress, indicating stability in model accuracy both at IoU \(\geq\) 0.5 and in the range [0.5, 0.95]. This behavior corroborates hypothesis H4\({}_{1}\), which posits a significant trend of convergence between metrics. The transition from low initial values to a region of higher density in the upper levels of the graph validates that the model reaches its optimal performance before the end of training, validating the robustness of the fitting process.