Abstract
As UAVs have become essential to military surveillance and operational strategies, our expertise addresses the increasing need for precise, real-time UAV deployment. The growth of UAVs raises several safety problems, necessitating systems capable of differentiating UAVs from non-UAVs, such as avian species. This study aims to identify a solution for the issue of quick aerial categorization across various situations without the need to rearrange devices, by analyzing and contrasting alternative models of advanced device mastery. We train and evaluate models utilizing extensive datasets, encompassing ANNs, SVMs, ensemble techniques, and RF-GBMs (Random Forests Gradient Boosting Machines). The models are assessed based on criteria that ascertain the feasibility of prospective real-time operations: accuracy, do-forget, and computational performance. Our findings indicate that Neural Networks significantly outperform birds, demonstrating remarkable precision in UAV recognition. This leads us to our main argument: Neural Networks significantly impact operational security and can substantially improve the distribution of defense resources. Our research indicates that machine learning is highly successful in real-time UAV recognition, significantly enhancing surveillance systems. Furthermore, it is advisable for military defenses to implement Neural Network systems to enhance decision-making capabilities and security operations. To remain competitive with increasingly agile and potentially covert drone designs, we anticipate advancements in UAV technology and recommend frequent model upgrades
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