• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.35-40
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• 2019

In this research, we suggest a real-time tele-driving system for unmanned drone operations using the LTE communication system. The drone operator is located 180km away and controls the altitude and position of the drone with a 50ms time delay. The motion data and video from the drone is streamed to the operator. The video is played on the operator's head-mounted display (HMD) and the motion data emulates the drone on the simulator for the operator. In general, a drone is operated using RF signal and the maximum distance for direct control is limited to 2km. For long range drone control over 2km, an auto flying mode is enabled using a mission plan along with GPS data. In an emergency situation, the autopilot is stopped and the "return home" function is executed. In this research, the immersion tele-driving system is suggested for drone operation with a 50ms time delay using LTE communication. A successful test run of the suggested tele-driving system has already been performed between an operator in Daejeon and a drone in Inje (Gangwon-Do) which is approximately 180km apart.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.619-627
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• 2021

Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test.

• Journal of Industrial Convergence
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• v.18 no.1
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• pp.79-85
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• 2020

Recently, the RPAS(Remote Piloted Aircraft System), by remote control and autonomous navigation, has been increasing in interest and utilization in various industries and public organizations along with delivery drones, fire drones, ambulances, agricultural drones, and others. The problems of the stability of unmanned drones, which can be self-controlled, are also the biggest challenge to be solved along the development of the drone industry. drones should be able to fly in the specified path the autonomous flight control system sets, and perform automatically an accurate landing at the destination. This study proposes a technique to check arrival by landing point images and control landing at the correct point, compensating for errors in location data of the drone sensors and GPS. Receiving from the Google Map API and learning from the destination video, taking images of the landing point with a drone equipped with a NAVIO2 and Raspberry Pi, camera, sending them to the server, adjusting the location of the drone in line with threshold, Drones can automatically land at the landing point.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1573-1587
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• 2021

A Drone image is an ultra-high-resolution image that is several or tens of times higher in spatial resolution than a satellite or aerial image. Therefore, drone image-based remote sensing is different from traditional remote sensing in terms of the level of object to be extracted from the image and the amount of data to be processed. In addition, the optimal scale and size of data used for model training is different depending on the characteristics of the applied deep learning model. However, moststudies do not consider the size of the object to be found in the image, the spatial resolution of the image that reflects the scale, and in many cases, the data specification used in the model is applied as it is before. In this study, the effect ofspatial resolution and image size of drone image on the accuracy and training time of the semantic segmentation deep learning model of six wintering vegetables was quantitatively analyzed through experiments. As a result of the experiment, it was found that the average accuracy of dividing six wintering vegetablesincreases asthe spatial resolution increases, but the increase rate and convergence section are different for each crop, and there is a big difference in accuracy and time depending on the size of the image at the same resolution. In particular, it wasfound that the optimal resolution and image size were different from each crop. The research results can be utilized as data for getting the efficiency of drone images acquisition and production of training data when developing a winter vegetable segmentation model using drone images.

• Journal of the Korean GEO-environmental Society
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• v.22 no.2
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• pp.35-39
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• 2021

Photogrammetry using drone can produce high-resolution ortho image and acquire high-accuracy 3D information, which is useful. Therefore, this study attempted to determine the possibility of using drone-photogrammetry in park construction by producing a topographic map using drone-photogrammetry and analyzing the problems and accuracy generated during production. For this purpose, we created ortho image and DSM (digital surface model) using drone images and created topographic status map by vectorizing them. Accuracy was compared based on topographic status map by GPS (global positioning system) and TS (total station). The resulting of analyzing mean of the residuals at check points showed that 0.044 m in plane and 0.066 m in elevation, satisfying the tolerance range of 1/1,000 numerical maps, and result of compared lake size showed a difference of about 4.4%. On the other hand, it was difficult to obtain accurate height values for terrain in which existed vegetation when producing the topographic map, and in the case of underground buried objects, it is not possible to confirm it in the image, so direct spatial information acquisition was necessary. Therefore, it is judged that the topographic status map using drone photogrammetry can be efficiently constructed if direct spatial data acquisition is achieved for some terrain.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.33-38
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• 2020

This paper proposes a technique for counting construction materials by analyzing an image acquired by a Drone. The proposed technique use drone log which includes drone and camera information, RCNN for predicting construction material type, dummy area and Photogrammetry for counting the number of construction material. The existing research has large error ranges for predicting construction material detection and material dummy area, because of a lack of training data. To reduce the error ranges and improve prediction stability, this paper increases the training data with a method of data augmentation, but only uses rotated training data for data augmentation to prevent overfitting of the training model. For the quantity calculation, we use a drone log containing drones and camera information such as Yaw and FOV, RCNN model to find the pile of building materials in the image and to predict the type. And we synthesize all the information and apply it to the formula suggested in the paper to calculate the actual quantity of material pile. The superiority of the proposed method is demonstrated through experiments.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.1003-1004
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• 2017

본 연구에서는 실내 환경에서 드론의 측위를 위한 마커 인식 및 검출 기술을 소개한다. 기존 실내 측위를 위한 기술인 Global Positioning System이나 Wi-Fi를 이용한 삼각측량 기법은 실내 환경에서 각각의 성질로 인하여 사용하기 어려운 점이 있다. 본 논문에서는 2차원 바코드와 마커 등의 객체를 드론의 카메라를 이용한 실시간 영상 전송을 통하여 검출하여 위치 정보를 획득하는 기술을 소개한다. 실험에서는 드론의 카메라를 통하여 실시간 전송된 영상에서 OpenCV V2.4.10을 통하여 객체를 검출하였고, 카메라와 객체 사이의 거리와 바코드 크기에 따른 2차원 바코드의 검출 여부를 보였으며 15*15cm의 2차원 바코드는 비교적 잘 인식하였으나 비교적 작은 11*11cm의 2차원 바코드는 거리가 멀어질 수록 인식이 힘들어지는 결과를 보였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.616-618
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• 2020

드론 탑재형 프로젝터 시스템의 경우 비행 시 드론의 모터와 프로펠러 그리고 비행 환경에 의해 발생하는 흔들림이 그대로 프로젝터에 전달되기 때문에 프로젝터에 의해 투영된 영상에 왜곡이 발생하게 된다. 이를 보정하기 위해 센서를 통해 얻어진 드론의 비행정보 기반 투영영상 변환행렬이 적용된다. 본 논문에서는 디스플레이 영역의 해상도를 고정된 값으로 제한하는 대신 비행 환경에 따라 해상도를 결정하는 방법을 제안하고 실제 영상에 적용하였다. 실험 결과, 제안한 디스플레이 영역의 해상도 최적화 방법을 적용하는 경우 기존의 고정된 디스플레이 영역의 해상도보다 확장된 디스플레이 영역의 해상도로 운용 가능함을 관찰할 수 있었다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.9
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• pp.854-864
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• 2016

The use of small drone platform has become a popular topic in these days but its application for SAR operation has been little known due to the burden of the payload implementation. Drone platforms are distinguished from the conventional UAV system by the increased vulnerability to the turbulences, control-errors and poor motion stability. Consequently, sophisticated motion compensation may be required to guarantee the successful acquisition of high quality SAR imagery. Extremely limited power and mass budgets may prevent the use of additional hardwares for motion compensation and the difficulty of SAR focusing is further aggravated. In this paper, we have carried out a feasibility study of mico-SAR drone operation. We present the image acquisition results from the preliminary flight tests and a quality assessment is followed on the experimental SAR images. The in-flight motion errors derived from the unique drone movements are investigated and attempts have been made to compensate for the geometrical and phase errors caused by motions against the nominal trajectory. Finally, the successful operation of drone SAR system is validated through the focussed SAR images taken over test sites.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.79-89
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• 2022

Images taken using drones have been applied to fields that require rapid decision-making as they can quickly construct high-quality 3D spatial information for small regions. To construct spatial information based on drone images, it is necessary to determine the relationship between images by extracting keypoints between adjacent drone images and performing image matching. Therefore, in this study, three study regions photographed using a drone were selected: a region where parking lots and a lake coexisted, a downtown region with buildings, and a field region of natural terrain, and the performance of AKAZE (Accelerated-KAZE), BRISK (Binary Robust Invariant Scalable Keypoints), KAZE, ORB (Oriented FAST and Rotated BRIEF), SIFT (Scale Invariant Feature Transform), and SURF (Speeded Up Robust Features) algorithms were analyzed. The performance of the keypoints extraction algorithms was compared with the distribution of extracted keypoints, distribution of matched points, processing time, and matching accuracy. In the region where the parking lot and lake coexist, the processing speed of the BRISK algorithm was fast, and the SURF algorithm showed excellent performance in the distribution of keypoints and matched points and matching accuracy. In the downtown region with buildings, the processing speed of the AKAZE algorithm was fast and the SURF algorithm showed excellent performance in the distribution of keypoints and matched points and matching accuracy. In the field region of natural terrain, the keypoints and matched points of the SURF algorithm were evenly distributed throughout the image taken by drone, but the AKAZE algorithm showed the highest matching accuracy and processing speed.