• 대한임베디드공학회논문지
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• 제13권6호
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.

• 한국위성정보통신학회논문지
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• 제12권4호
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• pp.26-33
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• 2017

드론은 원격 조종 또는 자동 조종을 통해 임무를 수행하는데 이때 무선통신 기술이 사용된다. 최근 무선통신 기술을 사용하여 드론을 악용하는 사례가 증가함에 따라 드론 RF 신호 탐지의 중요성이 증대되고 있다. 본 논문은 ISM(Industry Science Medical) 대역에서 Wi-Fi, Bluetooth 및 전용 드론 통신 방법을 고려한 시뮬레이션을 통해 효율적인 드론 RF 탐지 방식을 제안하였다. 일반 단말기와 드론 신호가 혼재한 환경을 구성한 뒤 드론 움직임에 따른 RF 특성을 이용하여 일반 단말기와 드론 신호를 구별하였다. 제안한 드론 RF 탐지 기법은 WRMD(Windowed RSSI Moving Detection) 연산과 Doppler Frequency 식별 방법이다. 시뮬레이션 환경은 2가지 신호와 4가지 신호가 혼재하는 환경으로 구성하였고 제안한 드론 RF 탐지 기법을 적용하여 드론 탐지율을 통해 성능을 분석하였다.

• 한국위성정보통신학회논문지
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• 제12권2호
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• pp.99-103
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• 2017

오늘날 드론의 대중화와 드론 관련 산업의 확장 등으로 인하여 드론 보급이 민 군에 걸쳐 증가하였고, 이와 더불어 보안, 안전사고, 치안 안보 위협 등의 우려도 함께 커지고 있다. 드론은 크기가 작고 반사도가 낮은 재질로 되어 있어 일반적인 센서로는 탐지가 어려운 것으로 알려져 왔다. 이에, 드론으로 인해 발생하는 사건 및 사고를 예방하기 위해서는 드론의 탐지와 위험 요소에 대응할 수 있는 기술에 대한 연구가 선행되어야 한다. 본 논문에서는 드론 탐지 기법을 분류하였다. 또한 CW 레이다를 기반으로 한 드론 탐지 실험을 통해, 마이크로 도플러의 패턴을 분석하여 드론 탐지의 가능성을 제시한다.

• 로봇학회논문지
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• 제12권4호
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• pp.425-431
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• 2017

As drones gain more popularity these days, drone detection becomes more important part of the drone systems for safety, privacy, crime prevention and etc. However, existing drone detection systems are expensive and heavy so that they are only suitable for industrial or military purpose. This paper proposes a novel approach for training Convolutional Neural Networks to detect drones from images that can be used in embedded systems. Unlike previous works that consider the class probability of the image areas where the class object exists, the proposed approach takes account of all areas in the image for robust classification and object detection. Moreover, a novel loss function is proposed for the CNN to learn more effectively from limited amount of training data. The experimental results with various drone images show that the proposed approach performs efficiently in real drone detection scenarios.

• 시스템엔지니어링학술지
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• 제19권2호
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• pp.126-134
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• 2023

Drones are emerging as a new security threat, and the world is working to reduce them. Detection and identification are the most difficult and important parts of the anti-drone systems. Existing detection and identification methods each have their strengths and weaknesses, so complementary operations are required. Detection and identification performance in anti-drone systems can be improved through the use of artificial intelligence. This is because artificial intelligence can quickly analyze differences smaller than humans. There are three ways to utilize artificial intelligence. Through reinforcement learning-based physical control, noise and blur generated when the optical camera tracks the drone may be reduced, and tracking stability may be improved. The latest NeRF algorithm can be used to solve the problem of lack of enemy drone data. It is necessary to build a data network to utilize artificial intelligence. Through this, data can be efficiently collected and managed. In addition, model performance can be improved by regularly generating artificial intelligence learning data.

• 반도체디스플레이기술학회지
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• 제20권3호
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• pp.117-119
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• 2021

With the development of the industry, the use of drones in specific mission flight is being actively studied. These drones fly a specified path and perform repetitive tasks. if the drone system will detect objects in real time, the performance of these mission flight will increase. In this paper, we implement object detection system and mount GPU acceleration to maximize the efficiency of limited device resources with drone video using Tensorflow Lite which enables in-device inference from a mobile device and Mobile SDK of DJI, a drone manufacture. For performance comparison, the average processing time per frame was measured when object detection was performed using only the CPU and when object detection was performed using the CPU and GPU at the same time.

• 전기학회논문지
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• 제68권2호
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• pp.359-363
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• 2019

As commercial drones have been widely used, concerns for collision accidents with people and invading secured properties are emerging. The detection of drone is a challenging problem. The deep learning based object detection techniques for detecting drones have been applied, but limited to the specific cases such as detection of drones from bird and/or background. We have tried not only detection of drones, but classification of different drones with an end-to-end model. YOLOv2 is used as an object detection model. In order to supplement insufficient data by shooting drones, data augmentation from collected images is executed. Also transfer learning from ImageNet for YOLOv2 darknet framework is performed. The experimental results for drone detection with average IoU and recall are compared and analysed.

• International Journal of Internet, Broadcasting and Communication
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• 제15권4호
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• pp.115-127
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• 2023

In this paper, we have undertaken a comprehensive investigation into the current state of anti-drone technology due to the increasing concerns and risks associated with the widespread use of drones. We carefully analyze anti-drone technology, dividing it into three crucial domains: detection, identification, and neutralization methods. This categorization enables us to delve into intricate technical details, highlighting the diverse techniques used to counter evolving drone threats. Additionally, we explore the legal and regulatory aspects of implementing anti-drone technology. Our research also envisions potential directions for advancing and evolving anti-drone tech to ensure its effectiveness in an ever-changing threat environment.

• 한국항행학회논문지
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• 제27권5호
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• pp.685-689
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• 2023

The proposed system in the study aims to detect forest fires in real-time stream data received from the drone-camera. Recently, the number of wildfires has been increasing, and also the large scaled wildfires are frequent more and more. In order to prevent forest fire damage, many experiments using the drone camera and vision analysis are actively conducted, however there were many challenges, such as network speed, pre-processing, and model performance, to detect forest fires from real-time streaming data of the flying drone. Therefore, this study applied image data processing works to capture five good image frames for vision analysis from whole streaming data and then developed the object detection model based on YOLO_v2. As the result, the classification model performance of forest fire images reached upto 93% of accuracy, and the field test for the model verification detected the forest fire with about 70% accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4795-4815
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• 2020

Drone detection can be considered as a specific sort of small object detection, which has always been a challenge because of its small size and few features. For improving the detection rate of drones, we design a Deeper SSD network, which uses large-scale input image and deeper convolutional network to obtain more features that benefit small object classification. At the same time, in order to improve object classification performance, we implemented the up-sampling modules to increase the number of features for the low-level feature map. In addition, in order to improve object location performance, we adopted the down-sampling modules so that the context information can be used by the high-level feature map directly. Our proposed Deeper SSD and its variants are successfully applied to the self-designed drone datasets. Our experiments demonstrate the effectiveness of the Deeper SSD and its variants, which are useful to small drone's detection and recognition. These proposed methods can also detect small and large objects simultaneously.