# Real-Time Image Analysis for Intelligent Aircraft De-Icing Decision Support Systems
## 3 Research Methodology  
The research process began with the collection and preparation of aerial photographs. The images were sourced from publicly available online resources. In Poland, regulations prohibiting the photographing of military objects, bridges, and airports were introduced on 17 August 2023. Therefore, the author of the study used photographs found on the internet. The images in the dataset depicted airplanes in winter conditions, showing varying degrees of icing and snow coverage. Preliminary results from previous research indicated that object classification depends on light intensity, dominant colors in the image, noise (falling snow), as well as image blur in situations where the lens is fogged up. Therefore, it was decided to create additional groups of photographs based on the collected dataset. For this purpose, image augmentation was used. The basic group consisted of 300 images. Based on this set, the following augmentation processes were performed:  
* Brightness process;
* Horizontal process;
* Hue Plus Minus Process;
* Noise process.  
In the Brightness process, the lighting parameter was reduced by 61%. Example images related to this process are shown in Figure 1.  
In the Horizontal process, a mirror reflection was applied to the images. Example images from the Horizontal process are shown in Figure 2.  
Figure 1: Image before modification (**a**) and image with the Brightness parameter adjusted (**b**).  
In the Hue Plus Minus process, additional hues were applied to the images. Example images from this process are shown in Figure 3.  
In the Noise process, noise was added to the images. The noise parameter was set to 10%. Example images are shown in Figure 4.  
In this way, the main set of images was increased from 300 to 520 photos. The dataset was balanced to reflect various environmental conditions. According to literature analysis, a balanced dataset allows for better object detection results [34]. Literature sources indicate that the minimum number of images required for reliable AI model training is approximately 200. Therefore, the number 520 was considered sufficient. Labels were added to each image in the final dataset. For this purpose, the Roboflow Universe platform was used. All labels were added manually. Although ice and snow are distinct phenomena, this study employs a single class due to their visual similarity and frequent co-occurrence (e.g., snow layers over ice). However, this design limits the method's effectiveness to  
Figure 4: Original image (**a**); image with noise at a level of 1.57% (**b**).  
Figure 3: Original image (**a**), image with a hue angle shift of \(-14^{\circ}\) (**b**), and image with a hue angle shift of \(14^{\circ}\) (**c**).  
Figure 2: Image before modification (**a**); image after horizontal flipping (**b**).  
scenarios where both appear simultaneously. When only one phenomenon occurs, the system cannot differentiate threat levels between dry snow, wet snow, or ice--a critical constraint for operational deployment.  
After the labeling process was completed, the dataset was divided into three subsets: a Train set with 480 images, Validation set with 20 images, and Test set with 20 images. Additionally, all images were converted to a size of 640 \(\times\) 640 pixels. In this way, a ready-to-use dataset was created, enabling the AI model training process to be carried out.