Boosting Aircraft Monitoring and Security through Ground Surveillance Optimization with YOLOv9

Abstract

The integration of object detection algorithms into aircraft tracking and ground surveillance systems presents a myriad of security advantages, bolstering the protection of critical infrastructure. These algorithms are instrumental in enforcing access control measures by continuously monitoring and discerning between authorized and unauthorized access to parked aircraft. With ongoing refinements, they serve as crucial components of intrusion detection systems, promptly alerting security personnel to any suspicious or unauthorized activities in the vicinity of grounded aircraft. Effective training of detection algorithms enhances their analytical capabilities, enabling them to discern between routine operations and security-threatening situations with greater precision. Notably, one of the pivotal applications lies in supporting digital forensic investigations, as these algorithms provide detailed activity logs, facilitating comprehensive post-incident analyses and bolstering forensic efforts to understand security incidents or breaches. In this investigation, we assessed the efficacy of the YOLOv9 detection algorithm in identifying aircraft situated on the ground surface. Furthermore, we highlight the significance of satellite imagery in dataset acquisition for object detection algorithms, particularly emphasizing the role of Low-Earth-Orbit (LEO) satellites in real-time image acquisition. Through this comprehensive analysis, we underscore the pivotal role of YOLOv9 in enhancing security measures and compliance with aviation security standards and regulations, ultimately fortifying the security posture within aviation environments.

Topics & Concepts