# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 3 Materials and Methods
### Main Features of the Model
#### 3.5.1 Architecture Details  
The YOLO11 architecture builds on its predecessors and incorporates an improved backbone and sensing head. The backbone consists of several convolutional layers with CSP (Cross Stage Partial) connections to improve gradient flow and reduce computational overhead. The network consists of:  
* Convolutional layers: The model includes a deep CNN with a variable number of convolutional layers depending on the version (n, s, m, l, x), ranging from 2.6 M to 56.9 M parameters [75].
* Size and number of filters: Convolutional layers use kernels of sizes 3 \(\times\) 3 and 5 \(\times\) 5, optimizing feature extraction at different spatial scales.
* Activation functions: The activation function used in all convolutional layers is Leaky ReLU, which guarantees nonlinearity and stable gradient flow.
* Pooling strategy: The model employs spatial pyramid pooling (SPP) to retain spatial information while efficiently reducing dimensionality.
* Regularization techniques: The training process integrates multiple regularization strategies, including dropout (0, 1) and label smoothing (0, 1), which prevent overfitting and improve generalization [76].  
The architecture of the YOLOv11 model used in this research is structured in three main modules: Backbone, Neck, and Head, as presented in Figure 4 [75].  
* Backbone: It is the module responsible for visual feature extraction. It is composed of multiple deep convolutional blocks with CSP (Cross Stage Partial) connections that optimize the gradient flow and reduce the computational cost. The convolutional layers employ 3 \(\times\) 3 and 5 \(\times\) 5 filters, with variable strides and adequate padding to preserve spatial resolution. In the base versions, the backbone contains approximately 30-40 convolutional layers. All layers are accompanied by batch normalization (Batch Normalization) and Leaky ReLU activation (\(\alpha\) = 0.1).
* Neck: The middle section of the model implements an optimized FPN (Feature Pyramid Network) and PANet mechanism, which allows effective feature combination at multiple scales. Operations such as concatenation, bilinear upsampling, and 1 \(\times\) 1 convolutions are included to adjust the dimensionality of the features. In addition, SPP (Spatial Pyramid Pooling) is incorporated to retain contextual information at different resolutions.
* Head: The final prediction layer performs simultaneous inference at three scales (P3, P4, P5), adjusted for small, medium, and large objects. The model employs anchor-freedetection, which improves flexibility and speed of inference. Each prediction includes box coordinates, confidence score, and classification. The total number of predictions per image varies according to the size of the feature map, with outputs generated through \(1\times 1\) convolutions and sigmoid activation functions.
* Regularization and optimization: During training, techniques such as Dropout (\(p=0.1\)) and Label Smoothing (\(\epsilon=0.1\)) are applied. The loss is calculated using a function composed of three components: CIoU loss for boxes, binary cross-entropy for classification, and objectness loss. AdamW optimizer with initial learning rate of 0.001 and cosine scheduler was employed.
* Implementation: The model was trained using PyTorch 2.0 and the Ultralytics YOLOv11 framework, run on an NVIDIA RTX 3090 GPU with 24 GB of VRAM, batch size of 16, for 300 epochs. Final model selection was performed with early stopping and cross validation.