Comparative Analysis of Military Detection Using Drone Imagery Across Multiple Visual Spectrums

2026-05-20

AI Summary

This study addresses the limited generalization capability of existing drone-based military target detection methods under complex battlefield conditions such as low visibility, nighttime operations, and thermal imaging. To this end, the authors present the first systematically constructed multispectral drone dataset encompassing four distinct modalities: grayscale, thermal, night vision, and Obscura Vision. Leveraging a unified framework based on the YOLOv11-small model, they conduct cross-spectral object detection experiments. Results demonstrate that the proposed approach substantially enhances model robustness, adaptability, and detection performance under extreme visual conditions, thereby improving the perceptual reliability of drones across diverse operational scenarios.

Abstract

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.

Problem

• military detection
• drone imagery
• visual spectrums
• object detection
• real-world scenarios

Innovation

• multi-spectral drone imagery
• military object detection
• YOLOv11-small
• adverse-condition vision
• KIIT-MiTA extension