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.
@article{arxiv.2605.21157,
title = {Comparative Analysis of Military Detection Using Drone Imagery Across Multiple Visual Spectrums},
author = {Sourov Roy Shuvo and Prajwal Panth and Rajesh Chowdhury and Sorup Chakraborty and Sudip Chakrabarty and Prasant Kumar Pattnaik},
journal= {arXiv preprint arXiv:2605.21157},
year = {2026}
}
Comments
6 pages, 7 figures. Accepted at the 16th International Conference on Computing, Communication and Networking Technologies (ICCCNT), July 6-11, 2025, IIT Indore. Proceedings pending publication