드론 환경에서 실시간 객체 탐지를 위한 딥러닝 네트워크 기술 동향 (Technology trends in deep learning networks for real-time object detection in drone environment)

문종현 (Jonghyeon  Mun); 손채봉 (Chae-Bong Sohn)

doi:10.37944/jams.v6i2.220

Technology trends in deep learning networks for real-time object detection in drone environment

Journal of Advances in Military Studies
Abbr : AdvMil
2023, 6(2), pp.181~196
DOI : 10.37944/jams.v6i2.220
Publisher : Institute of Defense Acquisition Program
Research Area : Social Science > Military Science > Other Military Science
Received : August 4, 2023
Accepted : August 30, 2023
Published : August 31, 2023

Jonghyeon Mun ¹, Chae-Bong Sohn ²

¹광운대학교 전자통신공학과
²광운대학교

Accredited

ABSTRACT

With the recent announcement of the Defense Innovation 4.0 Basic Plan by the Ministry of National Defense, the role and operational scope of drones are expanding as a key force in AI-based, unmanned, and autonomous systems. Consequently, the significance of real-time object detection technology is emphasized as drones take on diverse missions, including delivering, analyzing, and assessing real-time, target-related information. The emergence of recent deep learning has led to substantial advancements in the field of computer vision, particularly in object detection. Deep learning-based object detection is actively being researched, with a focus on algorithms suited for embedded and mobile environments such as drones. This research predominantly aims to develop deep learning-based object detection models that ensure real-time performance and accurately identify objects’ various forms and sizes. Recent object-detection models have been categorized into backbone networks, neck networks, and head networks. By utilizing these three network components, design considerations can be tailored to fulfill the requirements of drone operations. In this paper, we investigate the technology trends of deep learning network models that can be loaded into drones for real-time object detection. Thus we contribute to strengthening effective drone operation in military operations and supporting research and decision-making processes.

KEYWORDS

drones, computer vision, deep learning network, real-time object detection

Citation status

* References for papers published after 2025 are currently being built.

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Journal of Advances in Military Studies 2025 KCI Impact Factor : 1.0

Technology trends in deep learning networks for real-time object detection in drone environment

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Journal of Advances in Military Studies 2025 KCI Impact Factor : 1.0

Technology trends in deep learning networks for real-time object detection in drone environment

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