• Journal of Ecology and Environment
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• 제41권4호
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• pp.114-119
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• 2017

This study tried to analyze error range and resolution of drone images using a rotary wing by comparing them with field measurement results and to analyze stands patterns in actual vegetation map preparation by comparing drone images with aerial images provided by National Geographic Information Institute of Korea. A total of 11 ground control points (GCPs) were selected in the area, and coordinates of the points were identified. In the analysis of aerial images taken by a drone, error per pixel was analyzed to be 0.284 cm. Also, digital elevation model (DEM), digital surface model (DSM), and orthomosaic image were abstracted. When drone images were comparatively analyzed with coordinates of ground control points (GCPs), root mean square error (RMSE) was analyzed as 2.36, 1.37, and 5.15 m in the direction of X, Y, and Z. Because of this error, there were some differences in locations between images edited after field measurement and images edited without field measurement. Also, drone images taken in the stream and the forest and 51 and 25 cm resolution aerial images provided by the National Geographic Information Institute of Korea were compared to identify stands patterns. To have a standard to classify polygons according to each aerial image, image analysis software (eCognition) was used. As a result, it was analyzed that drone images made more precise polygons than 51 and 25 cm resolution images provided by the National Geographic Information Institute of Korea. Therefore, if we utilize drones appropriately according to characteristics of subject, we can have advantages in vegetation change survey and general monitoring survey as it can acquire detailed information and can take images continuously.

• 산업경영시스템학회지
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• 제46권3호
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• pp.259-267
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• 2023

At a time when the demand for drones is increasing, a plan to utilize drone images was sought for efficient promotion of cadastral resurvey. To achieve the purpose of this study, the technical and legal status of drone images was reviewed, and through this, the possibility of using it for cadastral resurvey was primarily reviewed. subsequently, an experiment was conducted targeting the project district to examine whether drone images were applied to boundary extraction, which is the primary process of cadastral resurvey. As a result of the experiment, it was found that boundary extraction from images is possible. However, in some cases, it is impossible due to field conditions or image quality. Therefore, it is necessary first to apply cases where boundary extraction is possible to cadastral resurvey and seek solutions for some impossible cases. In particular, the image quality problem may have problems with the current technology, but it will also have problems with the existing drone equipment. So, standard for drone calibration should also be established. Finally, the cadastral resurvey surveying procedure using drones was also presented

• 한국측량학회지
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• 제39권2호
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• pp.73-79
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• 2021

Recently there are growing interests on the energy conservation and emission reduction. In the fields of architecture and civil engineering, the energy monitoring of structures is required to response the energy issues. In perspective of thermal monitoring, thermal images gains popularity for their rich visual information. With the rapid development of the drone platform, aerial thermal images acquired using drone can be used to monitor not only a part of structure, but wider coverage. In addition, the stereo photogrammetric process is expected to generate 3D point cloud with thermal information. However thermal images show very poor in resolution with narrow field of view that limit the use of drone-based thermal photogrammety. In the study, we aimed to generate 3D thermal point cloud using visible and thermal images. The visible images show high spatial resolution being able to generate precise and dense point clouds. Then we extract thermal information from thermal images to assign them onto the point clouds by precisely establishing photogrammetric collinearity between the point clouds and thermal images. From the experiment, we successfully generate dense 3D thermal point cloud showing 3D thermal distribution over the building structure.

• 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.

• 융합정보논문지
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• 제9권12호
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• pp.17-23
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• 2019

본 논문에서는 미니 드론을 조종하면서 드론에 부착된 카메라가 촬영하는 영상을 실시간으로 받아들여 특정인의 얼굴을 인식하여 확인시켜주는 시스템 개발 방법론을 제안한다. 본 시스템의 개발을 위해서는 OpenCV, Python 관련 라이브러리 및 드론 SDK 등을 사용한다. 실시간 드론 영상으로부터 특정인의 얼굴 인식 비율을 높이기 위해서는 딥러닝 기반의 얼굴 인식 알고리즘을 사용하며 특히 Triples 원리를 활용한다. 시스템의 성능을 확인하기 위해 저자 얼굴을 기준으로 30회 동안 얼굴 인식 실험을 수행한 결과 약 95% 이상의 인식률을 보여주었다. 본 논문의 연구 결과물은 관광지, 축제 행사장 등에서 특정인을 드론으로 빠르게 찾기 위한 목적으로 사용할 수 있을 것으로 판단된다.

• 한국전자통신학회논문지
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• 제13권1호
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• pp.163-168
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• 2018

4차 산업혁명 시대의 도래와 함께 드론(Drone)의 활용이 다양한 분야에서 급속도로 진보하며 진행되고 있다. 이제 수사(搜査)의 영역에서도 드론이 광범위하게 활용될 것이다. 그동안의 형사사진이 2차원적 디지털 영상에 머물렀다면 드론을 활용할 경우 3차원 영상의 촬영뿐 아니라 그러한 영상물을 3D 프린터로 인쇄할 경우 사건현장의 재현이 가능하게 될 것이다. 일차적으로 수사기관이 드론을 이용하여 촬영하는 영상물은 디지털 영상증거로써 증거능력 확보를 위한 요건은 디지털 증거의 증거능력을 갖추기 위한 조건과 다르지 않다. 다만 드론이 과학수사의 새로운 영역으로 자리하게 될 때 드론으로 촬영한 영상물의 진정성을 확보하고 증거로써 활용할 수 있도록 시스템화 하는 것이 긴요한 바 본 논문에서는 드론으로 촬영한 디지털 영상물의 증거능력 확보방안을 제시하고자 한다.

• 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.

• 한국측량학회지
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• 제39권2호
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• pp.81-91
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• 2021

Drone related research has been increasing recently due to the development and distribution of commercial unmanned aerial vehicles. However, most of the previous studies focused on the accuracy and utility of drone surveying. For drones, the resolution of the result is determined according to the flight altitude, but since 70% of Korea is mountainous, it is necessary to analyze the quality of the drone image according to the flight plan. In this study, the quality of drone photogrammetry results according to flight plans was analyzed. The flight plan was established by fixed altitude and considering the height of the terrain. Images were acquired for both cases and data was processed to generate ortho images. As a result of evaluating the accuracy of the generated ortho image, the accuracy was found to be -0.07 ~ 0.09m. The accuracy of Case I and Case II did not show a significant difference, but for RMSE, Case I showed a good value. These results indicate that the drone flight plan affects the quality of the results. Also, when flying at a fixed altitude, II showed a lower value than the originally set overlap according to the altitude of the object. In future surveys using drones, flight planning taking into account the height of the object will contribute to the improvement of the quality of the results.

• 한국측량학회지
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• 제33권6호
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• pp.605-614
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• 2015

Drone imaging, which is more cost-effective and controllable compared to airborne LiDAR, requires a low-cost camera and is used for capturing color images. From the overlapped color images, we produced two high-resolution digital surface models over different test areas. After segmentation, we performed tree identification according to the method proposed by , and computed the tree height and the canopy crown size. Compared with the field measurements, the computed results for the tree height in test area 1 (coniferous trees) were found to be accurate, while the results in test area 2 (deciduous coniferous trees) were found to be underestimated. The RMSE of the tree height was 0.84 m, and the width of the canopy crown was 1.51 m in test area 1. Further, the RMSE of the tree height was 2.45 m, and the width of the canopy crown was 1.53 m in test area 2. The experiment results validated the use of drone images for the extraction of a tree structure.

• 한국측량학회지
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• 제40권3호
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• pp.147-157
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• 2022

최근 CCTV (Closed Circuit TeleVision)나 드론영상을 활용하여 인공지능 기반 예측 모델을 통해 차량을 분류하는 객체인식이나 교통량 분석을 하는 많은 연구들이 수행되고 있다. 정확한 교통량 추정을 위한 객체인식 딥러닝 모델을 개발하기 위해서는 체계적인 데이터 구축이 요구되는데 이와 관련된 표준화된 가이드라인은 미흡한 실정이다. 본 연구에서는 드론영상을 활용한 인공지능 기반 교통량 추정 학습데이터 구축 가이드라인 도출을 위하여 선행연구를 분석하고 사업보고서나 기존 인공지능 학습용 데이터 구축 및 품질관리 가이드라인을 참고하였다. 데이터 구축 가이드라인은 크게 데이터 획득, 가공, 검증으로 분류되며, 항목 별 유의사항 및 평가지표 가이드라인을 제시하였다. 본 연구의 결과물인 데이터 구축 가이드라인은 드론 영상 인공지능 기반 도로교통량 추정 분석을 하는데 강건하고 일반화된 인공지능 모델 개발에 도움을 제공하고자 한다.