# Real-Time Image Analysis for Intelligent Aircraft De-Icing Decision Support Systems
## 3 Research Methodology
### The Process of Training, Testing, and Validating Models
#### 3.1.2 Manual Training Process  
The same dataset as in the automatic process was used for AI model training. The training process was carried out in the Google Colab Pro environment with T4-NVIDIA CUDA GPU hardware acceleration (NVIDIA, Santa Clara, CA, USA). For manual training, 218 epochs were used--the same number as in the automated training, in order to compare the models. Data loading and preprocessing included scaling images to a size of 640 \(\times\) 640 pixels. The most important hyperparameters used for model training are presented in Table 1.  
The model training process was conducted manually in the Google Colab environment, based on selected hyperparameters. The analysis included key aspects of the training process, convergence, and model quality. In this way, performance metrics for the manually trained model were obtained. Figure 11 presents the quality metrics (precision, recall, mAP50, mAP50-95). \begin{table}
\begin{tabular}{c c c} \hline
**Hyperparameter Name** & **Value** & **Description** \\ \hline Batch size & 16 & Number of samples processed simultaneously in one training iteration Data. \\ Number of epochs & 218 & Number of full passes through the training dataset. \\ Image size & 640 & Input image resolution used during training. \\ Optimizer & AdamW (lr = 0.002) & Algorithm for updating model weights, here AdamW with lr = 0.002. \\ Momentum & 0.9 & Momentum factor used in the optimizer to \\ Loss function & box, cls, dfl loss & Losses for object detection: localization, classification, DFL. \\ Weight decay & 0.0005 & Regularization parameter to prevent overfitting. \\ \hline \end{tabular}
\end{table} 
Table 1: A summary of the key hyperparameters and their values used for model training. Figure 10: Images with detected objects (a–c). Quality metrics exhibit the following characteristics:  
* Precision: Increases from ~0.1 to 0.3-0.6, with noticeable fluctuations. This indicates the model is generating fewer false positives over time, though some instability persists;
* Recall: Rises from ~0.1 to 0.3-0.4, with variability similar to precision. The model becomes progressively better at detecting true snow and ice instances;
* mAP50: Mean average precision at an IoU threshold of 0.5 gradually improves to ~0.3, reflecting enhanced detection accuracy;
* mAP50-95: This more rigorous measure of precision across IoU thresholds (0.5-0.95) increases to ~0.15. It demonstrates moderate but steadily improving detection quality, even for stricter localization tasks. Figure 12 shows the progression of the Box Loss metric during training. The box loss curves for the training and validation sets reveal how the model learns to precisely localize objects (snow and ice) in images:  
Figure 11: Quality metrics of the manually trained model: (**a**) mAP50; (**b**) mAP50-95. Blue lines represent the progression of the metric values over successive epochs, while orange dots indicate the averaged values for each epoch. Figure 12: Box Loss metric for the manually trained model. Blue lines represent the progression of the metric values over successive epochs, while orange dots indicate the averaged values for each epoch. * Training: Box loss systematically decreases from above 2.0 to below 0.5 over 218 epochs, indicating effective adaptation to the training data. The smooth curve suggests stable learning without abrupt parameter shifts;
* Validation: An initial sharp spike (reaching \(\sim\)4.0) is observed, followed by rapid stabilization within the 2.3-2.7 range. The consistent validation loss level demonstrates that the model avoids overfitting and maintains generalization capabilities. Figure 13 shows the progression of the Class Loss metric. Class Loss reflects the model's ability to correctly classify detected objects:  
* Training: Class Loss drops sharply from \(\sim\)3.5 to below 0.5, indicating rapid learning of class-discriminative features;
* Validation: An initial spike (up to \(\sim\)30) occurs in early epochs, but values quickly drop and stabilize near training levels (\(\sim\)2-4).