# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 4 Results
### Stage 4: Data Processing and Transformation  
At this stage, data augmentation methods were implemented that included transformations such as rotation, scaling, translation, horizontal and vertical flipping, illumination manipulation, and noise addition. These techniques were applied randomly to each batch of images during training, allowing the generation of a virtually infinite set of unique data. The randomized process ensured that no two sets of images were exactly alike, avoiding overfitting and improving the generalizability of the model by introducing constant variability in the inputs. Each transformation was carefully parameterized to ensure  
Figure 6: (**a**) Diagram of possible positions to be located in a pit. (**b**) Image taken by the autonomous mobile station according to (**a**).  
Figure 7: Scheme of communication type.  
preservation of the essential features of the images while diversifying the visual patterns relevant to the model.  
Subsequently, the augmented images were processed through a Convolutional Neural Network (CNN), where multiple transformations were run through convolution layers. These layers applied the mathematical operation of convolution described in Equation (1):  
\[S(i,j)=\sum_{m}\sum_{n}I(i-m,j-n)K(m,n) \tag{1}\]  
In this operation, each two-dimensional convolution kernel K(m, n) interacted with a local region of the input image I(m, n) to produce a localized feature map S(i, j). This process enabled the detection of specific visual patterns, such as edges, textures, and shapes, in different regions of the images. The result of convolution in each layer produced multiple feature maps in parallel, each corresponding to a kernel detecting a specific feature. In addition, by sharing weights among the neurons in the network, the model significantly reduced the total number of parameters to be trained, improving computational efficiency and speeding up training.  
Each generated feature map encapsulated key information about the regions of interest in the processed images. This information was propagated through subsequent layers of the network by the operation defined in Equation (2):  
\[O_{i}(l)=b_{i}(l)+\sum_{j=1}^{n}K_{i,j}(l)\times I_{j}(l-1) \tag{2}\]  
where \(b_{i}(l)\) represents the polarization matrix, \(K_{i,j}(l)\) is the convolution kernel connecting the i-th feature map of the layer \(l\) with the j-th feature map of the previous layer, and \(I_{j}(l-1)\) corresponds to the input data of the previous layer. This mechanism allowed the extraction of hierarchical patterns, capturing low-level features in the first layers (edges and textures) and more abstract and specific features in the deep layers (complex shapes and structures).  
In synthesis, the joint application of data augmentation strategies and convolutional transformations succeeded in generating localized feature maps with high accuracy and diversity. This process not only robustized the training set, but also allowed the neural network to learn richer and more generalizable representations of the visual patterns present in the data. The integration of these techniques was critical to ensure optimal performance in the detection and classification tasks addressed in later stages.