# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 6 Conclusions  
This research demonstrates the transformative potential of artificial intelligence in the mining industry through the development and implementation of an autonomous mobile station capable of identifying and tracking equipment, people, and animals in critical operational areas. The proposed methodology, structured in five key stages, allowed us to comprehensively address the challenges associated with monitoring, safety, and optimization of mining processes. Key contributions include the integration of RGB and infrared cameras, dual communication systems, and deep learning algorithms that enabled real-time analysis and the issuance of automatic alerts, achieving unprecedented accuracy in real environments.  
The use of convolutional neural networks (CNNs) together with advanced data augmentation techniques was instrumental in achieving an average accuracy of 98% in the validation stage and 95% in the test stage, with outstanding results of 100% in specific classes such as 'digger' and 'drill.' These metrics validate the hypothesis that artificial intelligence-based models can overcome the limitations of traditional systems, offering more accurate and generalizable solutions. The sustained reduction of the loss function throughout training reflects an optimized model capable of balancing fit and generalization, which reinforces its ability to operate under diverse and demanding conditions.  
In addition to validating the model, this research sets a standard for future applications in the mining industry, including real-time monitoring, predictive maintenance, and the expansion of the system to other industrial sectors. The results obtained not only confirm the technical feasibility of this solution, but also underline its potential impact on reducing workplace accidents, improving operational efficiency, and promoting sustainable practices in a critical sector for the global economy.  
From a broader perspective, this research establishes a foundation for the evolution of intelligent, autonomous monitoring platforms in high-risk industrial domains. Future directions may include the integration of digital twins for real-time simulation, the use of generative AI for synthetic scenario modeling, and large-scale validation under varying geographies and climatic conditions. The convergence of AI, edge computing, and sustainable automation presented in this study positions the mining sector to lead in responsible technological innovation, enhancing not only safety and productivity but also long-term operational resilience.  
Conceptualization, G.H.-V. and G.P.C.; Formal analysis, G.H.-V.; Funding acquisition, G.P.C.; Investigation, G.H.-V., J.R.C.-H. and G.P.C.; Methodology, G.H.-V. and J.R.C.-H.; Project administration, J.R.C.-H.; Resources, J.R.C.-H. and G.P.C.; Software, G.H.-V. and J.R.C.-H.; Supervision, G.H.-V., J.R.C.-H. and G.P.C.; Validation, G.H.-V., J.R.C.-H. and G.P.C.; Visualization, G.H.-V.; Writing--original draft, G.H.-V.; Writing--review and editing, G.H.-V. All authors have read and agreed to the published version of the manuscript.  
We thank the grants from projects Universidad de la Costa (CUC), Barranquilla-Colombia. Through the researcher J.R.C.-H.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.