# Real-Time Image Analysis for Intelligent Aircraft De-Icing Decision Support Systems
## 4 Discussion  
After completing the learning processes and obtaining metrics for both models, the research results were compiled for comparison.  
Box Loss Metric for the automatic method (YOLO): Starts with a clear jump to values around 4.0, then stabilizes in the range of 2.5-3.0 for most of the learning process. Significant fluctuations are visible, which do not decrease as learning progresses.  
Box Loss Metric for the manual method (Colab): Shows a systematic decrease from 2.0 to below 0.5 for training data, which indicates better convergence. On the validation set, values stabilize around 2.4-2.5, similar to the automatic method.  
Performance metric (mAP, mAP50-95) for the automatic method: The mAP chart shows very high variability with clear oscillations (0.1-0.3), with a general upward trend finally reaching about 0.3. The mAP@50:95 metric increases more steadily to a value of about 0.15.  
Performance metric (mAP, mAP50-95) for the manual method: Achieves similar final values (mAP50 \(\approx\) 0.25-0.3, mAP50-95 \(\approx\) 0.15), but with smaller fluctuations. A more predictable learning curve with a clearer upward trend is also visible.  
Object Loss Metric for the automatic method: Object Loss quickly stabilizes at 3.0-3.5 after an initial jump to 7.5. No clear downward trend was visible in later epochs.  
CLS Loss and DFL Loss Metrics for the manual method: Both cls_loss and dfl_loss show a consistent decrease throughout the learning process. The smoothed curve indicates a more predictable optimization process.  
Despite differences in the learning process, both methods achieve similar final values for key quality metrics. For mAP (mAP50), both the automatic and manual methods achieve values of around 0.25-0.3 in the final epochs, with a slight upward trend. Similarly, for mAP@50:95 (mAP50-95), both methods stabilize at around 0.15. It is worth noting, however, that in the manual method, these values are achieved with significantly lower training losses, which may suggest better model generalization and less risk of overfitting. The study implemented two different classification methods, both achieving comparable F1 scores of approximately 0.32 and 0.33, demonstrating equivalent effectiveness of both approaches in the analyzed task.  
The values for obtained quality metrics such as mAP50 (0.25-0.3) and mAP50-95 (-0.15) may seem low from a theoretical point of view, but they are fully sufficient for practical application in a system cooperating with a camera mounted on a drone. The model does not analyze single, static images, but processes a video stream in which the camera records 30 frames per second. Even if the AI model recognizes an object only every third frame, the object will be detected in about 100 ms. In the case of better cameras that record 60 frames per second and allow analysis of every second frame, this time can be reduced to about 33 ms. This means that the object detection signal is generated at a high frequency, which is fully sufficient to trigger appropriate procedures, such as activating a trigger in the software. In practice, thanks to continuous video stream analysis and a high sampling frequency, the system minimizes the risk of missing a threat, thus ensuring sufficient reliability and effectiveness in applications related to aviation safety.