Transforming Aircraft Detection Through LEO Satellite Imagery and YOLOv9 for Improved Aviation Safety

The utilization of object detection algorithms in conjunction with satellite imagery to detect aircraft has become a crucial focus of investigation, bearing significant implications for the management of airports, as well as for the enhancement of aviation safety and security. Traditional monitoring methods, reliant on manual observation or radar systems, are laborintensive, error-prone, and may struggle to provide comprehensive coverage, particularly in vast or remote areas. In contrast, satellite imagery offers wide-area coverage and high-resolution imagery, ideal for capturing detailed views of airport facilities and surrounding areas. By coupling satellite imagery with advanced object detection algorithms like YOLO, significant improvements in aircraft detection capabilities are achievable. These algorithms streamline monitoring processes, enhance situational awareness, and enable prompt responses to potential safety or security threats, marking a paradigm shift in aircraft monitoring practices. Object detection algorithms, especially those based on deep learning techniques like convolutional neural networks (CNNs), have revolutionized object identification in images or video streams. Their adaptability to diverse environmental conditions and high accuracy make them invaluable across numerous applications, from surveillance to autonomous vehicles. Satellite imagery, particularly from Low Earth Orbit (LEO) satellites, offers exceptional detail and clarity, with shorter revisit times enabling near-real-time monitoring of dynamic events on the ground. Leveraging these advantages, this study focuses on enhancing airport security and aircraft safety through the detection of aircraft on the ground utilizing YOLOv9, chosen for its promising capabilities. While our findings demonstrate notable advancements, it is crucial to address certain limitations in the algorithm's detection capabilities to ensure its effectiveness in real-world applications.