• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.10
• /
• pp.197-205
• /
• 2020

Compared to aerial photogrammetry, UAV photogrammetry has advantages in acquiring and utilizing high-resolution images more quickly. The production of 3D models using UAV photogrammetry has become an important issue at a time when the applications of 3D spatial information are proliferating. Therefore, this study assessed the feasibility of utilizing 3D models produced by UAV photogrammetry through quantitative and qualitative analyses. The qualitative analysis was performed in accordance with the LODs (Level of Details) specified in the 3D Land Spatial Information Construction Regulation. The results showed that the features on planes have a high LoD while features with elevation differences have a low LoD due to the occlusion area and parallax. Quantitative analysis was performed using the 3D coordinates obtained from the CPs (Checkpoints) and edges of nearby structures. The mean errors for residuals at CPs were 0.042 m to 0.059 m in the horizontal and 0.050 m to 0.161 m in the vertical coordinates while the mean errors in the structure's edges were 0.068 m and 0.071 m in horizontal and vertical coordinates, respectively. Therefore, this study confirmed the potential of 3D models from UAV photogrammetry for analyzing the digital twin and slope as well as BIM (Building Information Modeling).

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.3
• /
• pp.693-698
• /
• 2018

This paper proposes a vehicle detection method for parking areas using unmanned aerial vehicles (UAVs) and using YOLOv2, which is a recent, known, fast, object-detection real-time algorithm. The YOLOv2 convolutional network algorithm can calculate the probability of each class in an entire image with a one-pass evaluation, and can also predict the location of bounding boxes. It has the advantage of very fast, easy, and optimized-at-detection performance, because the object detection process has a single network. The sliding windows methods and region-based convolutional neural network series detection algorithms use a lot of region proposals and take too much calculation time for each class. So these algorithms have a disadvantage in real-time applications. This research uses the YOLOv2 algorithm to overcome the disadvantage that previous algorithms have in real-time processing problems. Using Darknet, OpenCV, and the Compute Unified Device Architecture as open sources for object detection. a deep learning server is used for the learning and detecting process with each car. In the experiment results, the algorithm could detect cars in a dense area using UAVs, and reduced overhead for object detection. It could be applied in real time.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.6
• /
• pp.773-781
• /
• 2018

In recent years, a study of power-line inspection using an unmanned aerial vehicle (UAV) has been actively conducted. However, relevant studies have been conducting power-line inspection with an UAV operated by manual control, and they have developed just power-line detection algorithm on aerial images. To overcome limitations of existing research, we propose a drone-based power-line tracking system in this paper. The main contributions of this paper are to operate developed system under configured environment and to develop a power-line detection algorithm in real-time. Developed system is composed of the power-line detection and the image-based tracking control. To detect a power-line in real-time, a region of interest (ROI) image is extracted. Furthermore, clustering algorithm is used in order to discriminate the power-line from background. Finally, the power-line is detected by using the Hough transform, and a center position and a tilt angle are estimated by using the Kalman filter to control a drone smoothly. We design a position controller and an attitude controller for image-based tracking control, and both controllers are designed based on the proportional-derivative (PD) control method. The interaction between the position controller and the attitude controller makes the drone track the power-line. Several experiments were carried out in environments where conditions are similar to actual environments, which demonstrates the superiority of the developed system.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.22 no.1
• /
• pp.61-72
• /
• 2019

With the diversification of research using UAV(Unmanned Aerial Vehicle)s, the possibility of remote sensing research for urban green spaces is increasing. UAVs can be used as an investigation method to monitor the successful construction of the park and the planting of vegetation since its creation. This study was carried out to investigate UAVs utilization of urban green space monitoring in Dosol Square. It was photographed three times on May 21, July 13, and September 16, 2018 using DJI Phantom3 pro, Inspire2, and Parrot Sequoia multispectral camera. Orthographic images were overlaid on the planting plan of the site and the construction results were checked, the change of vitality of the plantation area was analyzed by NDVI(Normalized Difference Vegetation Index) and SAVI(Soil Adjusted Vegetation Index). As a result, it was confirmed that the UAVs are very effective for surveying the view of the urban green space after the construction and recording the results, which can be grasped quantitatively by overlaying the planting plan map. UAVs are more likely to be used in terms of monitoring vegetation vitality. It is interpreted that SAVI is better than NDVI in the green space just after composition. Chionanthus retusus and Pinus strobus were analyzed for their low level of vitality, and partially damaged and their vitality was lowered. In addition, there was difficulty in grass planting area and flower garden due to drainage and summer drought problems. In the future, it is expected that orthoimage and multispectral data using UAVs will be useful in the early vegetation monitoring and management field of urban green spaces.

• Journal of the Korea Society for Simulation
• /
• v.27 no.4
• /
• pp.19-26
• /
• 2018

The aerial launching UAV(Unmanned Aerial Vehicle) mainly uses a set of folding tandem wings to maximize flight performance and minimize the space required for mounting in a mothership. This folding tandem wing has a unique aerodynamic problem that is different from the general type of fixed wing aircraft, such as the rear wing interference problem caused by the wing of the front wing wake and vortex, and the imbalance of the pivot moment applied to the front and rear wings when the wing is deployed. In this paper, we have modeled and simulated various cases through computational fluid dynamics based on the finite volume method and analyzed various aerodynamic phenomena of the tandem wing type aircraft. We find that the front wing shall be installed higher than the rear for minimizing the wake influence and the rear wing can be deployed faster than the front because of the pivot moment due to aerodynamic forces. Also, considering the pivot moment due to aerodynamic force, the rear wing can be deployed much faster than the front wing. Therefore, it is necessary to consider it when developing the wing deploy mechanism.

• Korean Journal of Optics and Photonics
• /
• v.26 no.3
• /
• pp.155-161
• /
• 2015

We report the design and performance analysis of an off-axis three-mirror telescope as the fore optics for a new hyperspectral sensor aboard a small unmanned aerial vehicle (UAV), for low-altitude coastal remote sensing. The sensor needs to have at least 4 cm of spatial resolution at an operating altitude of 500 m, $4^{\circ}$ field of view (FOV), and a signal to noise ratio (SNR) of 100 at 660 nm. For these performance requirements, the sensor's optical design has an entrance pupil diameter of 70 mm and an F-ratio of 5.0. The fore optics is a three-mirror system, including aspheric primary and secondary mirrors. The optical performance is expected to reach $1/15{\lambda}$ in RMS wavefront error and 0.75 in MTF value at 660 nm. Considering the manufacturing and assembling phase, we determined the alignment compensation due to the tertiary mirror from the sensitivity, and derived the tilt-tolerance range to be 0.17 mrad. The off-axis three-mirror telescope, which has better performance than the fore optics of other hyperspectral sensors and is fitted for a small UAV, will contribute to ocean remote-sensing research.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.38 no.12
• /
• pp.1217-1222
• /
• 2010

This paper describes the Model-Based Development(MBD) process behind the flight control software of a close-range unmanned aerial vehicle(KUS-9). An integrated development environment was created using a commercial tool(MATLAB $Simulink^{(R)}$), which was utilized to design models for linear/nonlinear simulation, flight control law, operational logic and HILS(Hardware In the Loop Simulation) system. Software requirements were validated through flight simulations and peer reviews during the design process, whereas the models were verified through the application of a DO-178B verification tool. The integrity of automatically generated C code was verified by using a separate S/W testing tool. The finished software product was embedded on two different types of hardware and real-time operating system(uC/OS-II, VxWorks) to perform HILS and flight tests. The key findings of this study are that MBD Technology enables the development of a reusable and an extensible software product and auto-code generation technology allows the production of a highly reliable flight control software under a compressed time schedule.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.34 no.6
• /
• pp.619-627
• /
• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.

• Korean Journal of Remote Sensing
• /
• v.34 no.6_1
• /
• pp.1055-1066
• /
• 2018

Vegetation indices on the basis of optical characteristics of vegetation can represent various conditions such as canopy biomass and physiological activity. Those have been mostly developed with the large-scaled applications of multi-band optical sensors on-board satellites. However, the sensitivity of vegetation indices for detecting vegetation features will be different depending on the spatial scales. Therefore, in this study, the investigation of photochemical reflectance index (PRI), known as one of useful vegetation indices for detecting photosynthetic ability and vegetation stress, under the three spatial scales was conducted using multi-spectral camera installed in unmanned aerial vehicle (UAV),field spectrometer, and leaf reflectometer. In the leaf scale, diurnal PRI had minimum values at different local-time according to the compass direction of leaf face. It meant that each leaf in some moment had the different degree of light use efficiency (LUE). In early growth stage of crop, $PRI_{leaf}$ was higher than $PRI_{stands}$ and $PRI_{canopy}$ because the leaf scale is completely not governed by the vegetation cover fraction.In the stands and canopy scales, PRI showed a large spatial variability unlike normalized difference vegetation index (NDVI). However, the bias for the relationship between $PRI_{stands}$ and $PRI_{canopy}$ is lower than that in $NDVI_{stands}$ and $NDVI_{canopy}$. Our results will help to understand and utilize PRIs observed at different spatial scales.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2017.04a
• /
• pp.37-38
• /
• 2017

최근 방제 및 예찰과 같은 농작업에 단일 UAV(Unmanned Aerial Vehicle)시스템이 적용되고 있지만, 가반하중과 체공시간 등 기존시스템의 문제가 점차 대두되면서 작업 시간을 보다 단축시키고 작업 효율을 극대화 할 수 있는 농업용 멀티 UAV시스템의 필요성이 증대되고 있다. 본 논문에서는 작업자가 다수의 농업용 UAV를 효과적으로 제어할 수 있는 분산군집제어 알고리즘을 제안하며 알고리즘 검증 및 평가를 위한 시뮬레이터를 소개한다. 분산군집제어는 UAV 제어 계층, VP(Virtual Point) 제어 계층, 원격제어 계층으로 이루어진 3계층 제어구조를 가진다. UAV 제어 계층에서 각 UAV는 point mass로 모델링 되는 VP의 이상적인 경로를 추종하도록 제어한다. VP 제어 계층에서 각 VP는 입력 $p_i(t)=u^c_i+u^o_i+u^{co}_i+u^h_i$-(1)을 받아 제어되는데 여기서, $u^c_i{\in}{\mathbb{R}}^3$는 VP 사이의 충돌방지제어, $u^o_i{\in}{\mathbb{R}}^3$는 장애물과의 충돌방지제어, $u^{co}_i{\in}{\mathbb{R}}^3$는 UAV 상호간의 협조제어, $u^h_i{\in}{\mathbb{R}}^3$는 작업자로부터의 원격제어명령이다. (1)의 제어입력에서 충돌방지제어는 각 $u^i_c:=-{\sum\limits_{j{\in}{\eta}_i}}{\frac {{\partial}{\phi}_{ij}^c({\parallel}p_i-p_j{\parallel})^T}{{\partial}p_i}}$-(2), $u^o_c:=-{\sum\limits_{r{\in}O_i}}{\frac {{\partial}{\phi}_{ir}^o({\parallel}p_i-p^o_r{\parallel})^T}{{\partial}p_i}}$-(3)로 정의되면 ${\phi}^c_{ij}$와 ${\phi}^o_{ir}$는 포텐셜 함수를 나타낸다. 원격제어 계층에서 작업자는 햅틱 인터페이스를 통해 VP의 속도를 제어하게 된다. 이때 스케일변수 ${\lambda}$에 대하여 VP의 원격제어명령은 $u^t_i(t)={\lambda}q(t)$로 정의한다. UAV 시뮬레이터는 리눅스 환경에서 ROS(Robot Operating Systems)를 기반한 3차원 시뮬레이터인 Gazebo상에 구축하였으며, 마스터와 슬레이브 간의 제어 명령은 TCPROS를 통해 서로 주고받는다. UAV는 PX4 기반의 3DR Solo 모델을 사용하였으며 MAVROS를 통해 MAVLink 통신 프로토콜에 접속하여 UAV의 고도, 속도 및 가속도 등의 상태정보를 받을 수 있다. 현재 멀티 드론 시스템을 Gazebo 환경에 구축하였으며, 추후 시뮬레이터 상에 분산군집제어 알고리즘을 구현하여 검증 및 평가를 진행하고자 한다.