# Real-Time Image Analysis for Intelligent Aircraft De-Icing Decision Support Systems
## 2 Literature Review
This approach reduces resource waste and delays while enhancing safety. Future solutions will likely combine vision systems with plasma technologies and automated fluid application systems, guided by AI analyzing real-time sensor data. Hybrid systems aim to improve energy efficiency and responsiveness to icing threats [26; 27; 28]. There are vision systems for detecting aircraft icing and snow. One such system is a model based on DETR (Detection Transformer) technology, which has been applied to detect icing and snow on aircraft surfaces. This system uses self-attention mechanisms, allowing for precise analysis of spatial relationships between ice, snow, and the structure of the aircraft. To improve the precision of icing boundary localization, an additional RefineBox optimization network was used. Studies conducted on a custom image dataset demonstrated the high effectiveness of this system in various environmental conditions, such as different lighting angles or the presence of clear ice and icicles. This model achieved a high detection accuracy, making it a promising tool for practical operational use at airports. Another approach is the use of laser systems for detecting and removing ice. This technology uses laser radiation at a wavelength that is preferentially reflected by the aircraft surface but absorbed by ice, snow, and water. This enables precise monitoring of surface temperature and identification of areas covered with ice. The system also allows for mapping the thickness of the ice and documenting its location using thermal cameras. These technologies can be used both for detection and real-time removal of ice [29]. Another example is the Smart Ice Control System based on machine learning, which utilizes various artificial intelligence models such as logistic regression, support vector machines (SVM), and neural networks (Multilayer Perceptron). This system analyzes data from temperature and humidity sensors to predict ice formation in real time. By employing supervised and unsupervised learning algorithms, it enables not only precise ice detection but also optimization of de-icing processes, enhancing operational safety and reducing energy consumption [30]. Intelligent Vision Systems (IVSs) technology uses diffuse light imaging and artificial intelligence to instantly detect ice both in the air and on the ground. This system enables the identification of ice crystals and snow located beneath the surface being de-iced before takeoff. This technology supports pilots in making operational decisions and helps aircraft manufacturers meet regulatory requirements for ice detection [31]. Vision systems used in aviation can be significantly enhanced by artificial intelligence (AI), especially in the context of detecting snow and ice on aircraft surfaces. AI models,based on machine learning algorithms, have the potential to automatically recognize snow coverage, identify its type, and assess the degree of threat to flight operations. However, building such a model requires considerable effort, starting with collecting a sufficiently large training dataset that includes images of aircraft in various weather conditions, followed by their detailed analysis and labeling. It is also crucial to take into account the variability of lighting conditions, camera angles, and the diversity of snow types. The author of this article undertakes the challenge of developing such an AI model, being aware of its complexity, but at the same time recognizing the enormous potential that the use of artificial intelligence brings to de-icing decision-making processes. With a properly trained AI model, it will be possible to increase the precision of snow detection on aircraft and optimize de-icing procedures, which will contribute to improved flight safety and operational efficiency in aviation [32,33].