# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 5 Discussion  
The results of this research demonstrate a remarkable level of robustness and generalizability in the proposed system, especially in its ability to identify and track critical mining assets in real operating conditions. The model's average accuracy of 98% in validation and 95% in testing not only confirms the effectiveness of the learning architecture but also reinforces its suitability for real-time deployment in high-risk mining environments. Exceptional results in specific categories such as excavators and drills, which achieved 100% classification accuracy, can be attributed to well-defined geometric patterns, consistent spatial features, and larger training sample volumes for these classes. The integrated use of convolutional neural networks (CNNs) with advanced data augmentation techniques was instrumental in the generalizability of the model, particularly in complex terrains with varying illumination and occlusion. Precision-recall analysis provided further evidence of the classifier's discriminative power: high precision indicates a low false positive rate, while high recall values confirm a low incidence of false negatives,  
Figure 14: Heat graph of the evolution of the metrics. especially in dominant classes. However, an imbalance between classes was observed. Categories with fewer training samples, such as support machinery or personnel-related instances, showed fluctuations in detection consistency, likely due to under-representation during training. This phenomenon is consistent with prior findings in deep learning applications for industrial object detection, where class imbalance remains a persistent challenge [51, 52]. Comparative studies in the literature support these observations. For instance, YOLOv8 models applied to UAV inspection and steel defect detection environments demonstrated similar performance gaps between dominant and minority classes [77, 78]. Likewise, ASF-YOLO and Gold-YOLO improved detection precision using multiscale attention and gather-distribute modules, but faced reduced performance in low-data scenarios [48, 50]. These comparisons underscore the relevance of incorporating class-sensitive learning strategies, such as focal loss and weighted sampling, which have already proven successful in domain-specific datasets with inherent distribution biases [79, 80]. Moreover, the training dynamics observed in this study--including the consistent decline and stabilization of the loss function--indicate robust convergence behavior and effective regularization, achieved through techniques such as dropout and label smoothing. This reinforces the system's potential for transferability to similar operational contexts, such as open-pit mining, tunneling, or construction monitoring. In contrast to fixed-position AI systems or traditional CCTV setups, the autonomous mobile station designed here offers advantages in coverage adaptability, operational autonomy, and computational decentralization via edge processing. Similar findings have been reported in deployments of YOLO-integrated platforms for risk-prone environments, confirming the efficacy of mobile AI units for scalable and real-time monitoring [73, 74]. However, several operational limitations must be taken into account. Environmental factors such as dust, vibration, and signal interference can affect sensor performance and data transmission, especially in adverse weather conditions or low visibility. Furthermore, although the dual-band (RF and 5 GHz) communication system improves redundancy, latency spikes or bandwidth saturation in dense topographies remain potential limitations. In future work, the integration of multi-modal sensing (LiDAR, thermal imaging, acoustic data) and federated learning approaches could improve robustness and privacy, especially for deployments in regulated environments. Additionally, predictive analytics, facilitated by long-term data logging and anomaly pattern recognition, can pave the way for AI-driven preventive maintenance.