对比无人机多光谱图像检测军事目标，提升实战环境下的识别能力。
Comparative Analysis of Military Detection Using Drone Imagery Across Multiple Visual Spectrums
- 构建灰度、热成像、夜视、模糊四类模拟真实战场的视觉数据集
- 基于YOLOv11-small模型在四种场景下实现稳定军事目标检测
- 为攻防任务中无人机侦察系统提供可落地的多场景检测方案
现代战争中，无人机已成为情报收集和精确打击的关键工具，可在多种敌对环境中实时、远程执行任务。本文基于KIIT-MiTA数据集，针对实际复杂环境的多样性，构建了四类模拟真实条件的数据集：灰度图像、热成像、夜视图像和模糊视觉图像，分别对应低能见度、热辐射、夜间及遮蔽场景。采用YOLOv11-small模型在上述多场景下进行训练与测试，评估其检测性能。实验表明该模型在不同视觉条件下均保持较高鲁棒性，显著提升了无人机在复杂战场环境下军事目标的识别准确率与可靠性。研究成果为防御与进攻任务中的智能感知系统提供了关键技术支撑。
原文摘要 · Abstract (English)
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.
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