In modern warfare, drones are becoming an essential part of intelligence gathering and carrying out precise attacks in different kinds of hostile environments. Their ability to operate in real-time and hostile environments from a safe distance makes them invaluable for surveillance and military operations. The KIIT-MiTA dataset is comprised of images of different military scenarios taken from drones, and these provide a foundation for detecting military objects, but it does not take into account the various types of real-world scenarios. With that in mind, to evaluate how the models are performing under varying conditions, four different types of datasets are created: Gray Scale, Thermal Vision, Night Vision, and Obscura Vision. These simulate the real-world environments such as low visibility, heat-based imagery, and nighttime conditions. The YOLOv11-small model is trained and used to detect objects across diverse settings. This research boosts the performance and reliability of drone-based operations by contributing to the development of advanced detection systems in both defensive and offensive missions.

翻译：在现代战争中，无人机已成为敌对环境中情报收集与精准打击的核心装备。其具备在安全距离外实时执行侦查与军事任务的特性，对军事监视与作战行动具有重要价值。KIIT-MiTA数据集虽包含无人机拍摄的多种军事场景影像，为军事目标检测提供了基础，但未涵盖现实场景中的各类复杂情况。为此，本研究建立了四种模拟真实环境的变体数据集：灰度视觉、热成像视觉、夜视视觉与遮蔽视觉，分别模拟低能见度、热基成像及夜间条件等场景。采用YOLOv11-small模型进行多场景目标检测训练与评估。该研究通过提升防御与进攻任务中先进检测系统的性能与可靠性，为无人机作战系统的优化提供了技术支撑。