# Real-Time Image Analysis for Intelligent Aircraft De-Icing Decision Support Systems
## 1 Introduction  
The problem of aircraft icing and snow accumulation is one of the most serious challenges facing modern aviation. It directly affects both operational safety and economic efficiency. Ice on aerodynamic surfaces, such as wings or stabilizers, disrupts airflow, leading to reduced lift, increased aerodynamic drag, and difficulties in controlling the aircraft. Icing can also increase the aircraft's weight, shift its center of gravity, and block the operation of systems such as control surfaces, brakes, or sensors. In extreme cases, this can lead to loss of control of the aircraft. Additionally, ice on windshields reduces visibility, posing an additional hazard during takeoff and landing. During takeoff, icing lengthens the takeoff run and increases the risk of stalling, while during flight, conditions involving freezing rain or drizzle are particularly dangerous, as they can quickly create a thick layer of ice that is difficult to remove [1, 2, 3]. Snow in aviation is less problematic than ice, but it can still negatively affect the organization of flight operations. Dry snow usually does not adhere permanently to the aircraft's surface, while wet snow at temperatures between 0 \({}^{\circ}\)C and 5 \({}^{\circ}\)C can quickly turn into ice on the airframe, creating hazards. Additionally, heavy snowfall can reduce visibility and make takeoffs and landings more difficult. Regardless ofthe type of snow (safe or dangerous), safety procedures are undertaken to remove it. The risk of snow accumulation and icing depends on the region of the world. Regions particularly at risk of icing and snow accumulation are primarily polar areas, such as the Arctic and Antarctic, where extreme weather conditions are common. However, problematic weather conditions affect many regions around the world [4; 5; 6].  
Icing has been the cause of many aviation disasters over the years. Examples include the crash of a Boeing 721 in Iran in 2011 and the tragic accident of Fuerza Aerea Uruguaya Flight 571 in the Andes in 1972. These events highlight the importance of effective anticiping methods for aviation safety [7; 8]. Modern anti-icing methods include both traditional mechanical and chemical procedures as well as innovative technologies. Vision systems enable the rapid detection of ice or snow on aircraft surfaces and support real-time de-icing decisions. Advanced solutions use optical sensors and image analysis algorithms to provide detailed information about the aircraft's condition [9].  
Vision systems are increasingly supported by artificial intelligence models [10]. Therefore, an attempt was made to develop an artificial intelligence (AI) model capable of automatically recognizing snow and ice on aircraft surfaces. This process required creating an appropriate training dataset and training the model based on various weather scenarios and operational conditions. Currently, there is no ready-made AI model dedicated to this purpose, which makes this task particularly ambitious and innovative. Developing such a solution could significantly improve the effectiveness of ice detection and the optimization of de-icing procedures, thereby contributing to increased aviation safety and operational efficiency in challenging weather conditions. At present, these are preliminary studies and will be further developed in the future [11; 12].