# 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
This dynamic suggests the model initially struggled to distinguish classes on the validation data but adapted swiftly. Figure 14 shows the progression of the Distribution Focal Loss metric. The DFL (Distribution Focal Loss) process involves determining the accuracy of bounding box localization predictions during training and validation. During training,  
Figure 14: Distribution Focal Loss metric progression for the manually trained model. Metric progression for the training process (**a**), metric progression for the validation process (**b**). Blue lines represent the progression of the metric values over successive epochs, while orange dots indicate the averaged values for each epoch. Figure 13: Class Loss metric progression 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. a clear, systematic decline from values above 2.0 to below 0.5 was observed, indicating progressively improving localization precision (Figure 13). In validation, an initial peak (up to -epoch 9) followed by rapid stabilization in the 3-4 range was noted, consistent with trends for box_loss and cls_loss. A gradual improvement in object localization precision (decrease in dfl_loss) was observed in the DFL charts, which is a key indicator for object detection models, especially in applications requiring precise marking of icing areas on aircraft. In order to evaluate the effectiveness of the model, the values of precision and recall on the validation set were recorded after each training epoch. Based on these results, charts were created showing changes in precision and recall as a function of the number of epochs. The charts are presented in Figure 15.  
For the given values of precision and recall, the calculated F1 score was 0.33. Figure 16 shows sample images from the validation process performed in manual mode. The manual learning process in Colab proceeded stably: all key losses (box, class, dfl) on the training set show a systematic decrease without signs of overfitting. On the validation set, initial loss peaks are visible, but the model quickly stabilizes, indicating  
Figure 15: Change in precision (**A**) and recall (**B**) as a function of the number of epochs during manual model training. proper generalization. The quality metrics (precision, recall, mAP) show an increasing trend, although with greater variability than in automatic systems, which may result from manual hyperparameter selection and less process automation. The model achieves final mAP50 values of approximately 0.3 and an mAP50-95 of approximately 0.15, comparable to the results obtained in the automatic process. The model was trained on 218 epochs in 0.592 h, which indicates a relatively efficient training process. The model architecture consists of 72 layers and contains 11,125,971 parameters, operating with an efficiency of 28.4 GFLOP. The training was conducted on a Tesla T4 graphics card (15,095 MiB of memory).