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

TL;DR

This paper evaluates drone-based military object detection across multiple visual spectrums using diverse datasets and the YOLOv11-small model to improve operational reliability in various conditions.

Contribution

It introduces a comprehensive analysis of detection performance across different visual environments using the YOLOv11-small model on multiple drone imagery datasets.

Findings

Detection performance varies significantly across different visual spectrums.

Thermal and Night Vision datasets present unique challenges for object detection.

The study demonstrates the effectiveness of YOLOv11-small in diverse military 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.