드론 환경에서 실시간 객체 탐지를 위한 딥러닝 네트워크 기술 동향
Technology trends in deep learning networks for real-time object detection in drone environment
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초록
최근 국방부가 국방혁신 4.0 기본계획을 발표하면서 AI 기반 무인ㆍ자율체계의 핵심 전력으로 드론의 역할과 운용 범위가 확대되고 있다. 이에 드론은 실시간으로 표적 관련 정보를 전달, 분석, 평가하는 것은 물론 고도의 정보 수집 등 다양한 임무를 수행하게 되면서 실시간 객체 탐지 기술의 중요성이 강조되고 있다. 최근 딥러닝 기술의 등장으로 컴퓨터 비전 분야, 특히 물체 감지 분야에서 상당한 발전이 이루어졌다. 이에 따라 드론과 같은 임베디드 및 모바일 환경에 적합한 알고리즘을 중심으로 딥러닝 기반의 객체 감지 기술이 활발히 연구되고 있다. 이러한 연구는 실시간 성능을 보장하고 다양한 형태와 크기의 물체를 정확하게 식별하는 딥러닝 기반의 물체 감지 모델 개발을 주된 목표로 한다. 최근의 딥러닝 기반 실시간 객체 검출 모델은 백본 네트워크, 넥 네트워크, 헤드 네트워크로 분류할 수 있다. 이 세 가지 네트워크 구성 요소를 활용하여 특정 운영 요구사항에 맞춰 드론 운영 요구사항을 충족하도록 설계 고려사항을 조정할 수 있다. 본 논문에서는 실시간 객체 감지를 위해 드론에 탑재할 수 있는 딥러닝 네트워크 모델의 기술 동향을 알아보고, 이를 통해 군사 작전과 국가안보 분야에서 효과적인 드론 운용을 강화하고 연구 노력과 의사 결정 과정을 지원하는 데 기여할 것으로 기대된다.
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.
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