• Journal of the Korean Geographical Society
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• v.50 no.5
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• pp.473-483
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• 2015

In a coastal area, a plenty of research has adopted remotely sensed data. This is because longterm interaction between land and ocean makes continuous geographical changes in a broad extent and unaccessible areas. However, conventional remote sensing platforms such as satellite or airplane has several disadvantages including limited temporal resolution and high operational costs. Hence, this study uses a UAV system to detect a coastline and its movement. Result of coastline detection shows how the coastline moves in a day. Time-series coastlines were derived from UAV aerial images through digital image processing. There is a drawback in the stability of UAV compared to the conventional remote sensing platform, but the advantage appears on the economical efficiency. Since the latest studies shows an improvement of UAV for a variety of purposes in many fields, a UAV can also be utilized for regional study and spatial data acquisition platform. geography can also utilize a UAV as a spatial data acquisition platform for regional study.

• Explosives and Blasting
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• v.35 no.1
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• pp.18-26
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• 2017

In Analysis of Fragmentation of rock blasted, The photo analysis method has been mainly used and these image acquisitions are mainly obtained by digital image from the front of the crushed rock. However, Image analysis is basically advantage of the image of planar shooting not front shooting but There is no way to take a photograph of huge plane rock slope. Thus, Unavoidably It is resolved by distorting or extending the image filmed at the front as well as adjusting it similar to its angle of plane shooting. Lately, With advancing unmanned aerial vehicle, It can simply image the fragment conditions of blasted rock of a high-definition digital image and Through it, It can acquire the most planar image to angle which accumulate cataclastic rock and also can make image analysis. In this study, It has been confirmed that tolerance value of analysis result of image filmed flatly is markedly lower than the existing front filmed image.

• Smart Media Journal
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• v.5 no.2
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• pp.51-58
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• 2016

Recently, the unmanned aerial vehicle Drone technology is attracting new interest around the world. The versatilities in science, military, marketing, sports, and entertainment fields are the driving force of the drone fever. Thus, the potential power of future industrial is expected as the application range is extensive. In this paper, we design and propose the prototype of unmanned aerial vehicle-based bigdata processing system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.372-378
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• 2016

The purpose of this study is to develop a system configuration for gathering data and building a database for agriculture. Some foreign agriculture-related companies have already constructed such a database for scientific agriculture. The hardware of this system is composed of automatic capturing equipment based on aerial photography using a UAV. The software is composed of parts for stitching images, matching GPS data with captured images, and building a database of collected weather information, farm operation data, and aerial images. We suggest a method for building the database, which can include information about the amount of agricultural products, weather, farm operation, and agricultural land images. The images of this system are about 5 times better than satellite images. Factors such as farm working and environmental factors can be basic data for analyzing the full impact of agriculture land. This system is expected to contribute to the scientific analysis of Korea's agriculture.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.275-275
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• 2019

기후변화에 따른 집중호우의 발생빈도와 강도가 증가하면서 대규모 홍수로 인한 인명 및 재산피해가 발생하고 있다. 그에 따라 홍수 상황을 신속하게 확보하고 홍수피해를 빠르게 예측하는 모니터링 기술이 필요하다. 최근 공간정보 분야에서 무인항공기 (UAV: Unmanned aerial vehicles)를 이용한 3차원 지형자료 확보 연구가 활발하게 이용되고 있다. 무인항공기는 지형자료 구축 뿐 만 아니라 홍수 시 신속한 홍수 모니터링이 가능하기 때문에, 본 연구에서는 무인항공기를 이용하여 홍수 전 지형자료 구축을 비롯하여, 홍수 시 모니터링, 홍수 후 지형자료 구축에 이르기까지 UAV를 이용한 홍수 모니터링 기술을 소개한다. 연구대상지는 금강 합류 직전 논산천 하류 1 km 지점으로, UAV를 이용한 지형자료를 구축하기 이전에 좌표 매칭을 위한 GCP (Ground Control Point ) 측량을 실시하고, UAV 비행계획을 수립하고 촬영한다. 촬영된 영상을 GCP좌표와 소프트웨어 (Pix4D)를 이용하여 정사영상과 DSM(Digital Surface Model)자료를 구축한다. 홍수시 UAV를 이용한 촬영을 통하여 동영상은 수재해 플랫폼에 송신하고, 이미지 영상은 홍수 전 영상처리와 동일한 방법을 이용하여 지형 자료를 구축하여, 홍수시 침수심이나 지형변화를 분석한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.317-325
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• 2021

Pine wilt disease first appeared in Busan in 1998; it is a serious disease that causes enormous damage to pine trees. The Korean government enacted a special law on the control of pine wilt disease in 2005, which controls and prohibits the movement of pine trees in affected areas. However, existing forecasting and control methods have physical and economic challenges in reducing pine wilt disease that occurs simultaneously and radically in mountainous terrain. In this study, the authors present the use of a deep learning object recognition and prediction method based on visual materials using an unmanned aerial vehicle (UAV) to effectively detect trees suspected of being infected with pine wilt disease. In order to observe pine wilt disease, an orthomosaic was produced using image data acquired through aerial shots. As a result, 198 damaged trees were identified, while 84 damaged trees were identified in field surveys that excluded areas with inaccessible steep slopes and cliffs. Analysis using image segmentation (SegNet) and image detection (YOLOv2) obtained a performance value of 0.57 and 0.77, respectively.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.7
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• pp.576-585
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• 2014

In this paper, we survey the Moving Target Indication(MTI) techniques for small UAVs. MTI consists of image alignment phase and frame differencing correction phase, and image alignment has two ways of parametric approach which is mainly focused in this paper and non-parametric approach. Since small UAVs are operated in the low altitude, the parallax is considerable and the epipolar geometry is applied to compensate the parallax. The related works and future works are presented.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.829-846
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• 2018

Crop monitoring can provide useful information for farmers to establish farm management strategies suitable for optimum production of vegetables. But, traditional monitoring has used field measurements involving destructive sampling and laboratory analysis, which is costly and time consuming. Unmanned Aerial vehicle (UAV) could be effectively applied in a field of crop monitoring for estimation of cultivated area, growth parameters, growth disorder and yield, because it can acquire high-resolution images quickly and repeatedly. And lower flight altitude compared with satellite, UAV can obtain high quality images even in cloudy weather. This study examined the possibility of utilizing UAV in the field of crop monitoring and was to suggest the application method for production of crop status information from UAV.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1209-1216
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• 2020

In this study, we propose a method for detecting coastal surface wastes using an UAV(Unmanned Aerial Vehicle) remote sensing method and an object detection algorithm based on deep learning. An object detection algorithm based on deep neural networks was proposed to detect coastal debris in aerial images. A deep neural network model was trained with image datasets of three classes: PET, Styrofoam, and plastics. And the detection accuracy of each class was compared with Darknet-53. Through this, it was possible to monitor the wastes landing on the shore by type through unmanned aerial vehicles. In the future, if the method proposed in this study is applied, a complete enumeration of the whole beach will be possible. It is believed that it can contribute to increase the efficiency of the marine environment monitoring field.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.511-523
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• 2023

Research on the integration of unmanned aerial vehicles and deep learning for reinforced concrete damage detection is actively underway. Convolutional neural networks have a high impact on the performance of image classification, detection, and segmentation as backbones. The MobileNet, a pre-trained convolutional neural network, is efficient as a backbone for an unmanned aerial vehicle-based damage detection model because it can achieve sufficient accuracy with low computational complexity. Analyzing vanilla convolutional neural networks and MobileNet under various conditions, MobileNet was evaluated to have a verification accuracy 6.0~9.0% higher than vanilla convolutional neural networks with 15.9~22.9% lower computational complexity. MobileNetV2, MobileNetV3Large and MobileNetV3Small showed almost identical maximum verification accuracy, and the optimal conditions for MobileNet's reinforced concrete damage image feature extraction were analyzed to be the optimizer RMSprop, no dropout, and average pooling. The maximum validation accuracy of 75.49% for 7 types of damage detection based on MobilenetV2 derived in this study can be improved by image accumulation and continuous learning.