Drone Detection: Countering Optical Evasion Strategies Using Deep Learning

In the dynamic realm of aerial surveillance, detecting drones, especially those equipped with advanced camouflage techniques, is an escalating challenge. This study delves into the detection of camouflaged drones, examining various concealment methods, such as drone-birds and commercial and military drones, that are meticulously designed to merge with their surrounding environment. Given the lack of existing datasets to address these intricate scenarios, we have created a new dataset considering evasion strategies. This dataset covers various variables, including angles, technologies, and environmental conditions. Through this approach, we aim to boost the efficiency of drone detection systems against the sophisticated evasion tactics employed by various drone models. Our method not only enhances detection rates under diverse conditions but also lays the groundwork for future research in aerial surveillance technology. By merging these synthetic datasets with current detection frameworks, we showed how the detection systems perform in order to detect the evading drone.