複数の視覚スペクトルにおけるドローン画像を用いた軍事目標検出の比較分析
Comparative Analysis of Military Detection Using Drone Imagery Across Multiple Visual Spectrums
ドローン画像の軍事目標検出において、グレースケール、熱画像、暗視、オブスキュラ視覚など様々な環境を模擬したデータセットを作成し、YOLOv11-smallモデルで検出性能を比較評価した研究。
著者: Sourov Roy Shuvo, Prajwal Panth, Rajesh Chowdhury, Sorup Chakraborty, Sudip Chakrabarty, Prasant Kumar Pattnaik
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
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.