Research On The Efficiency Of The Optical Method For Detection Of Unmanned Aircraft Using Popular Yolo Machine Learning Algorithms

Abstract

The modern world is experiencing rapid technological advancements, and one of the key areas where this progress is particularly noticeable is the use of unmanned aerial vehicles (UAVs). These technologies find applications in various fields, ranging from agriculture to military uses. However, along with numerous advantages, UAVs also present new challenges, particularly in the field of security. The development and implementation of effective systems for UAV detection are critical tasks for ensuring the security of critical infrastructure and preventing the potential illegal use of drones. The research findings highlight the importance of selecting the optimal YOLO model for specific UAV application scenarios, integrating it with other detection systems, and developing new strategies to improve the overall efficiency of detection systems. These results are significant both for the advancement of artificial intelligence technologies and for enhancing security levels in critical areas of human activity.