A_Study_on_the_Advancement_of_Intelligent_Military.pdf — page 9

M. Lee et al.: Intelligent Military Drone: A Case Study of a Reconnaissance Drone
The graph shows that the pink line is the result of re-
inforcement learning using the observational data presented
in this study. It shows an upward trend in both cumulative
rewards and episode length, suggesting effective learning.
The blue line, on the other hand, represents the outcomes
of learning using only the vector data such as confidence
level, camera angle, drone coordinates, and speed, excluding
the image from the original observational data. This line
shows a relatively milder upward trend. When comparing
the results between the two lines from 3 to 5 million steps,
where the result begins to stabilize, the pink line shows an
average cumulative reward and episode length of 230 and
987, respectively, while the blue line shows 137 and 734. This
difference indicates that using images effectively enhances
the drone’s intelligence.
Additionally, the experimental results hold the following
significance. For drones, the real-time processing and cate-
gorization of numerous objects encountered, including roads,
trees, and structures, present significant challenges due to
constraints on battery life and computational power. However,
the experiments show that using images with light object
detection algorithms without the need for complex prepro-
cessing can still lead to a high degree of intelligence.
D. COMPREHENSIVE APPLICATION AND VALIDATION OF
TECHNOLOGIES
Finally, we were able to confirm that the series of processes,
which include using rule-based waypoints for reconnaissance
and tracking upon enemy detection, followed by activating
reinforcement learning to enhance detection confidence level,
proceeded smoothly. The following explains how the process
was implemented and verified through Figure 12. In Figure
12(a), the waypoint setup is indicated in yellow, and the
drone and tank hidden in a structure are marked with sky-
blue circles. Figure 12(b) shows the view of the drone in the
sky from the perspective of an enemy on the ground. Figure
12(c) depicts the start of the drone’s reconnaissance mission.
The text in the top right of the figure indicates the altitude
and step count, a concept of discrete time. Here, it’s evident
that the drone maintains an altitude of about 150 meters. As
the drone patrols the designated waypoints, it discovers a
tank as shown in Figure 12(d). Then, as seen in Figure 12(e),
the drone moves directly to track the tank, positioning it at
the center of the drone camera’s image. Up to this point,
before the activation of the reinforcement learning neural
network, the camera continuously faces directly downward.
Once the tank is centered in the drone’s camera view, the
confidence level is checked to see if it exceeds the arbitrarily
set benchmark of 90% specifically designated for this study.
Here, the confidence level is below 90%, so the reinforcement
learning neural network is activated as shown in Figure 12(f).
This allows the drone to move and adjust the camera angle
towards the tank, securing visibility and ultimately increasing
the confidence level to over 90%.
(a) Setting waypoints
(b) Ground-to-air view
(c) Start reconnaissance
(d) Tank detection
(e) Tracking tank
(f) Precise calibration
FIGURE 12. Validation of technologies application according to scenario
in Unity.
IV. CONCLUSION
This paper offers an engineering perspective within the mil-
itary field on using drones with object detection capabilities
for military purposes. This is explored through a case study
based on specific scenarios. When thinking about using smart
drones in the military, it’s important to consider factors such
as the complexity of the environment, efforts by enemies to
interfere, lack of data, the need for stable operation, and the
provision of information for making attack decisions. The
environmental complexity in this scenario is highlighted by
the task of identifying enemies concealed under structures
or trees in varied terrains, including urban and mountainous
regions. In fulfilling this mission, the drone is programmed
to maintain a certain altitude to enhance the detection of
hidden enemies and to safeguard against unforeseen threats.
To address the challenge of data scarcity, we used synthetic
data generated by the Unity simulator alongside real image
data that has been augmented, utilizing both to enhance the
overall dataset. For operational stability, the drone’s flight,
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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