# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 2 Literature Review  
Recent advances in vision-based deep learning have established a robust technological framework for critical applications in industrial, mining, and environmental sectors. In industrial settings, MoistNet demonstrated high accuracy in measuring moisture content in organic materials such as wood chips, enhancing quality control in production lines [34]. Similarly, transformer-based vision models have improved body pose estimation in heavy machinery, offering operational advantages for autonomous mining environments [35]. From a safety perspective, deep convolutional architectures have enabled accurate modeling of smoke dispersion, supporting proactive risk management in hazardous environments [36]. End-to-end visual systems for autonomous driving have shown promising adaptation to high-risk transport scenarios, contributing to operational mobility in extraction contexts [37]. In quality assurance, explainable causal deep learning models have increased the resilience of inspections under visual interference, addressing common challenges in industrial visual analytics [38]. Notably, real-time monitoring platforms for excavators in open-pit mining have leveraged computer vision to optimize earthmoving operations and productivity metrics [39]. Complementary efforts in structural health monitoring, synthetic image detection, and additive manufacturing supervision further validate the strategic role of vision-based deep learning in automating complex, data-rich tasks [40; 41; 42]. Collectively, these contributions underscore the relevance of architectures such as YOLOv11 for autonomous operations in mining, offering scalable precision, real-time responsiveness, and environmental adaptability in safety-critical industrial domains. Figure 2: Research trend of the thematic axis in recent years. In parallel, YOLO-based object detection architectures have exhibited significant evolution in scale-awareness, latency control, and domain specialization. Models like YOLO-MS and Mamba YOLO have refined hierarchical feature learning and sequence modeling for real-time object recognition [43; 44]. Context-aware adaptations such as YOLO*C and YOLO-NL have addressed weaknesses in non-local attention and occlusion handling, optimizing performance in environments with low visibility or visual clutter [45; 46]. Domain-specific versions like YOLO-Facev2 and ASF-YOLO have successfully adapted to facial recognition and cellular segmentation under constraints of scale and noise [47; 48]. In agriculture, models such as YOLO-Granada and E-YOLO have enabled the detection of pomergnate fruits and estrus states in livestock with superior generalization in open environments [49; 50]. Industrial variants such as RDD-YOLO and Gold-YOLO have optimized surface defect recognition and attention redistribution to improve mean average precision with minimal computational cost [51; 52]. Reviews of YOLO architectures provide strategic guidance for model selection and adaptation across diverse use cases, including UAV inspection, manufacturing lines, and risk monitoring [53; 54]. Fast R-CNN and its derivatives have likewise advanced real-time detection and classification tasks across multiple high-impact fields. In healthcare, these models have been pivotal in the diagnosis of dental caries [55], dermatological lesions [56], diabetic foot ulcers [57], and depression through facial expression analysis [58]. Medical segmentation and anatomical landmark detection have also benefited from 3D-enhanced architectures and anatomical feature modeling [59; 60]. In environmental monitoring, Fast R-CNN has improved cyclone detection [61], seismic landslide prediction [62], and smoke diffusion modeling [59], showcasing scalability in dynamic visual conditions. For industrial automation, applications range from fruit detection in UAVs using optical and 3D fusion [63] to mobile robotic grasping through enhanced Faster R-CNN architectures [64].