A_Study_on_the_Advancement_of_Intelligent_Military.pdf — page 4

M. Lee et al.: Intelligent Military Drone: A Case Study of a Reconnaissance Drone
FIGURE 3. Intelligent drone reconnaissance scenario and applied technologies.
Military drones are classified according to their purpose of
use into reconnaissance, attack, deception, electronic warfare,
and as targets [38], [39]. In this case study, we focus on the
intelligence of reconnaissance drones. The drone in the case
study is modeled after the drone of the Korean Poongsan com-
pany, as shown in Figure 2. The model can perform a variety
of tasks depending on the applied modules. However, in this
study, a drone equipped with reconnaissance modules is used.
The module includes sensors and systems such as cameras,
LRF, GNSS, and others. For specification assumptions, it is
presumed that the drone is capable of being equipped with an
object detection and reinforcement learning neural network.
A. SCENARIO
The scenario assumes specific conditions and objectives
for research purposes. An intelligent reconnaissance drone,
equipped with the capability to detect enemies, is deployed
for the mission. The drone’s task is to locate and track enemy
tanks, identified as key targets, in a designated sector during
clear daytime weather conditions. After tracking, the drone
provides information about these tanks to the final decision-
makers for attack planning. It is assumed that a communi-
cation system has been pre-established between the drone
and the decision-makers, allowing for data transmission and
reception. The sector assigned for the drone’s reconnaissance
contains trees and structures, offering potential concealment
or camouflage for the tanks. Additionally, there is a risk of the
drone being shot down if it flies too low, due to the possibility
of unexpected anti-aircraft fire from the enemy within the
sector.
B. TECHNOLOGY APPLIED IN THE SCENARIO
Given such a scenario, what specific technologies could be
applied to make the drone intelligent? Let’s examine this
based on Figure 3. The technologies mentioned here are
not yet at a stage where they can be implemented in real-
world environments. Therefore, it is important to note that
all subsequent content discussed has been implemented and
tested within the Unity simulator.
1) Firstly, as drones need "eyes" for intelligence, we apply
the lightweight algorithm YOLOv4-tiny, considering
the limited computational speed and memory of em-
bedded systems. This choice is also influenced by the
fact that YOLOv4-tiny is a relatively recent algorithm
that is compatible with the Unity simulator.
2) Drones, with cameras facing downward from above,
require top-view images of tanks for effective object
detection. However, most publicly available online re-
sources predominantly feature frontal photos of tanks.
Getting pictures of military equipment from specific
angles is challenging due to security reasons. There-
fore, this study uses synthetic data from Unity and
augmented real images. The training and validation
data are divided into three categories, which include
real images, synthetic data, and augmented data derived
from real images. Among these categories, the real
images consist of 1180 tank photos taken from various
angles, including 30 aerial view tank photos, which
are similar in size to those observed by drones. We
use 700 images of Unity synthetic data and create 400
augmented images from 30 real aerial view photos of
tanks. These 400 augmented images are further divided
into two groups where the first consists of 150 images
transformed through methods like geometric resizing,
Gaussian blur, image inversion, color inversion, and
grayscale conversion, and the second comprises the re-
maining 250 images augmented using Alias-Free Gen-
erative Adversarial Networks, known as StyleGAN3
[40]. The test dataset uses 100 aerial view real images
not included in the training and validation data. As
shown in Table 1, the best performance is achieved
when real images, Unity synthetic data, and augmented
data are combined.
TABLE 1. Training results by dataset.
Dataset
F1 Score
AP (Average Precision)
Real
0.85
0.90
Real, Synthetic
0.85
0.91
Real, Synthetic, Augmentation
0.89
0.94
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VOLUME 11, 2023
This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and 
content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3390035
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4