AI-Powered Real-Time Runway Safety: UAV-Based Video Analysis with ICSO-Enhanced Deep Learning

Abstract

In the aviation sector, ensuring safe landings while prioritizing the safety of runways is crucial to prevent accidents and incidents during the landing phase of flights. However, many studies analyzing unsafe events, such as runway cracks or inadequate friction, often fail to quantify their impacts on flight safety during landing. In airport pavement management systems (APMS), the condition of the runway surface is a critical factor in ensuring the operational safety of aircraft during take-off and landing. Therefore, it is essential to provide pilots with reports on runway conditions, including measurements of surface performance, to support informed decision-making. To tackle these challenges, we propose a real-time automatic monitoring system for runway safety utilizing video analysis. Specifically, we employ a time-series analysis approach using the improved chameleon swarm optimization (ICSO) algorithm to mine runway surface characteristics from real-time video data captured by unmanned aerial vehicles (UAVs). Subsequently, we introduce the fuzzy reinforced polynomial neural network (FR-PNN) to detect risks in runway surface characteristics, enabling automatic monitoring to enhance the safety of aircraft landings. Finally, the effectiveness of the proposed system is validated using real-time videos obtained from Bechyne military airport, located in Bohemia. This system aims to improve runway safety by providing timely and accurate assessments of runway conditions, thereby facilitating safer landings for aircraft.