# Real-Time Image Analysis for Intelligent Aircraft De-Icing Decision Support Systems
## 5 Conclusions  
The automatic method, utilizing the YOLO architecture, was characterized primarily by a high configuration speed, as it requires minimal manual intervention in hyperparameter selection. This solution is optimized for computational efficiency, enabling rapid deployment and effective operation, which is crucial in real-time processing applications. There is little control over parameter settings during model preparation. In the context of atmospheric phenomena classification, a single detection class for ice and snow was used due to their frequent co-occurrence and visual similarity, which enables rapid hazard identification. However, this approach does not allow for distinguishing between different water phases (e.g., dry or wet snow) and requires additional validation when implemented in flight safety systems, representing a compromise between detection speed and precision. Additionally, higher fluctuations in loss functions such as Box Loss or Object Loss observed in the charts may help avoid becoming stuck in local minima, which can be beneficial when working with complex datasets. The automatic method also allows less advanced users to develop a model. In contrast, the manual method, implemented in the Google Colab environment, enabled better convergence, as evidenced by a systematic and smooth decrease in training losses to lower final values. The training process was more stable, and fluctuations in performance metrics such as precision, recall, or mAP were smaller compared to the automatic method. A significant advantage of the manual approach is the ability to precisely control and adjust parameters during training, allowing for better responses to specific challenges encountered when working with data. The choice of the appropriate method should depend on the specifics of the project. The automatic method (YOLO) is best when rapid model deployment is essential, expert resources are limited, real-time operation is required, and the training data is high quality and well describes real cases. The manual method is more appropriate when maximum model precision is required, which is crucial in the case of aircraft icing detection. Manual model training is particularly justified in situations where there are specific requirements regarding error characteristics, such as the need to minimize false alarms. This approach is also effective when expert knowledge is used for advanced model parameter tuning, and when working with heterogeneous data that requires an individual approach and flexibility in the modeling process. The difference in precision is primarily due to the advanced, automated optimization and standardization procedures available in Roboflow, which are difficult to manually replicate in Google Colab without significant experience and knowledge of model optimization. Both methods achieve similar final results; however, the manual method provides better control over the training process, lower final loss values, and shorter model training time. In the context of detecting ice and snow on aircraft, where safety is paramount, the manual method is recommended, especially when expert resources are available. In aircraft ice detection, it is important not only to detect the presence of ice but also to precisely determine its location. To obtain more reliable and useful results, it is advisable to increase the number of photographs and conduct further research on the models. It is recommended to divide the detection process into classes corresponding to specific parts of the aircraft, such as wings, fuselage, engines, or propellers. Such studies will be carried out by the author in the future. This research received no external funding. **Institutional Review Board Statement: Not applicable. **  
**Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to copyright restrictions on the images developed by the author.