# Real-Time Image Analysis for Intelligent Aircraft De-Icing Decision Support Systems
## 3 Research Methodology
### The Process of Training, Testing, and Validating Models
#### 3.1.1 Automatic Training Process  
The YOLOv8 (Ultralytics) framework is an advanced, stable model for object detection, segmentation, and classification, utilizing a CNN architecture with anchor-free detection and C2f modules for high precision and speed. The framework is available in variants from yolov8n (nano) to yolov8x (extra-large), and this project used yolov8s to balance performance and speed. Implemented in Python 3.9 with PyTorch v.2.6.0 and Roboflow cloud servers, YOLOv8 automatically localized and classified snow/ice on aircraft in images. Training was conducted on the Roboflow platform v.2025 in Accurate mode, ensuring a high detection precision. Roboflow's automated environment streamlined data preparation and parameter optimization. The training process was fully automated, with Roboflow dynamically adjusting key hyperparameters (epochs, batch size, learning rate) based on data characteristics and training mode. Training lasted 45 min and 18 s, influenced by model architecture and computational resources. Post-training, Roboflow generated a detailed report, evaluating the model using metrics like mean Average Precision (mAP) and mAP@50:95. The evolution of these metrics is illustrated in Figure 5.  
The detection performance graph for the automated model can be divided into four phases:  
* Early phase (0-40 epochs): Unstable values with sharp fluctuations in mAP;
* Intermediate phase (40-120 epochs): Gradual increase in mAP from ~0.15 to 0.25, accompanied by pronounced oscillations;
* Advanced phase (120-200 epochs): Stabilization of mAP within the 0.2-0.25 range;
* Final phase (>200 epochs): Systematic rise to ~0.3, indicating progressive model generalization. Figure 5: Precision metric changes during the model training process. The parallel, more gradual increase in mAP@50:95 (lighter line), reaching values around 0.15 in the final training phase, indicates a robust detection quality across varying IoU thresholds. The Box Loss graph, illustrating changes in the bounding box localization loss function over successive training epochs, has been shown in Figure 6.  
The observed learning process exhibited the following characteristic phases:  
* Initial phase (0-20 epochs): Sharp increase to a maximum value of \(\sim\)4.1, followed by a rapid decline to \(\sim\)3.0;
* Stabilization phase (20-80 epochs): Values oscillated within the 2.5-3.0 range, with visible fluctuations;
* Maturity phase (80-200 epochs): Relatively stable level around 2.5-2.7, showing a slight downward trend;
* Final phase (200-218 epochs): Slight increase followed by a decline, suggesting the attainment of a local minimum. Stabilization of Box Loss at approximately 2.5 indicates the model's effective learning of geometric parameter determination for regions of interest, which is critical for precise localization of ice-snow deposits on aircraft surfaces. An Object Loss graph, illustrating changes in the loss function related to object detection, is shown in Figure 7.  
Figure 7 consists of three main phases:  
* Initial phase: Sharp increase to approximately 7.5, followed by a rapid decline. * Intermediate phase (20-150 epochs): Stabilization at a level of 3.0-3.5. * Final phase: Slight increase and eventual stabilization at approximately 3.5. Figure 6: Loss function change graph. Figure 7: Object loss graph during the model training process. Stabilization of Object Loss indicates that the model has reached an optimal equilibrium between detecting true objects and generating false positives. The Class Loss graph, shown in Figure 8, illustrates the process of classification error minimization. In the initial phase of the graph, a sharp initial spike is visible. An extremely high value of around 270 occurs near the 10 th epoch. This is followed by rapid convergence: a drastic drop to below 10 by the 20 th epoch. From this point on, long-term stabilization occurs: from around the 25 th epoch, the value remains close to zero.