• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.5
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• pp.51-65
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• 2024

With the advancement of Unmanned Aerial Vehicles (UAV) technology, aerial spraying has been rapidly increasing in the agricultural field. Drones offer many advantages compared to traditional applicators, but they pose challenges such as spray drift risk and spray uniformity. To address these issues, it is essential to understand the characteristics of complex airflow generated by drones and its consequences for the spray performance. This study aims to identify the air velocity distribution of drone downwash and the resulting spray deposition distribution on the ground, ultimately proposing optimized spraying widths and criteria. Experiments were conducted using two agricultural drones with different propeller arrangements under various flight and measurement conditions. The results showed that during hovering, the downward airflow affected the area within a distance of the radius of the blade (R) from the center of the drone. When the drone was flying, the downward airflow was effective up to a distance of 2R. Droplet deposition was concentrated at the center of the drone during hovering. However, during flying, the droplet deposition was more evenly distributed up to the distance of R. The drone downwash and droplet deposition were significantly different during flying compared to the hovering state. At an effective spray width of 3R, the coefficient of variation (CV) was generally less than 16%, indicating a significant improvement in spray uniformity. These findings help optimize effective spraying techniques in drone-based applications.

• The Korean Journal of Air & Space Law and Policy
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• v.35 no.2
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• pp.75-111
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• 2020

The civilian drone world has evolved in recent years from one dominated by hobbyists to growing involvement by companies seeking to profit from unmanned flight in everything from infrastructure inspections to drone deliveries that are already subject to regulations. Drone flight under the property right relation with the land owner would be deemed legal on the condition that expeditious and innocent passage of drone flight over the land be assured. The United Nations Convention on the Law of the Sea (UNCLOS) enshrines the concept of innocent passage through a coastal state's territorial sea. Passage is innocent so long as it is not prejudicial to the peace, good order or security of the coastal state. A vessel in innocent passage may traverse the coastal state's territorial sea continuously and expeditiously, not stopping or anchoring except in force majeure situations. However, the disturbances caused by drone flight may be removed, which is defined as infringement against the constitutional interest of personal rights. For example, aggressive infringement against privacy and personal freedom may be committed by drone more easily than ever before, and than other means. The cost-benefit analysis, however, has been recognjzed as effective criteria regarding the removal of disturbances or injunction decision. Applying that analysis, the civil action against such infringement may not find suitable basis for making a good case. Because the removal of such infringement through civil actions may result in only the deletion of journal article. The injunction of drone flight before taking the information would not be obtainable through civil action, Therefore, more detailed and meticulous regulation and criteria in public law domain may be preferable than civil action, at present time. It may be suitable for legal stability and drone industry to set up the detailed public regulations restricting the free flight of drone capable of acquiring visual information amounting to the infrigement against the right of personal information security.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.251-262
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• 2017

The system of magnetic exploration with a drone flight was constructed and applied to the iron mine site. The magnetic probe system installed on the drone used a sensor as Bartington's fluxgate type magnetometer, Mag639 and the A/D converter to collect magnetic intensity values on the tablet PC. The drone flight control module is a highly expandable Pixhawk with allowing 15 minutes of flight by loading 3kg. Experiments on the magnetic field interference range were performed to remove the erroneous effect from the drone with applying RTK GPS to obtain the magnetic intensity value at the accurate position. The accurate location information enabled to obtain the gradient measurement of magnetic field by measuring twice at different altitudes. Also, by using the terrain information, we could eliminate the terrain effect by setting the flight path to fly along the terrain. These results are in line with the field experiments using the nuclear proton magnetometer G-858 of Geometrics Co., Ltd, which adds to the reliability of the drone based aeromagnetic survey system we constructed.

• Journal of Digital Convergence
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• v.16 no.8
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• pp.71-79
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• 2018

This study discussed the meaning of a drone, and especially drone journalism and legal and ethical issues around that, at an introductory dimension, which is used in various social bases, but is still just an academic discussion at the beginning stage. As a methodology, content analysis was used. It seems that drone journalism has high diffusibility as a technology with high 'relative advantage', 'compatibility', 'trial ability' and 'observability' and low 'complexity' in terms of the diffusion of innovation theory. However, it will be very likely that controversies will be raised, such as safety issue due to collision and crash, a dispute over violation of privacy that may seriously infringe privacy like individual portrait rights and a controversy about the accuracy and source of information as drone filming low price and ease of use. Suggest solutions to legal and ethical issues based on existing research. Technical stability is required. Also, it is necessary to change the awareness of journalists about the drones coverage and to educate ethics, and it is necessary to establish social public opinion on issues such as privacy violation and establish system and legal measures through it. Future research is expected to carry out empirical research including journalists and public awareness surveys.

• 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.

• 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.

• Ecology and Resilient Infrastructure
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• v.8 no.1
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• pp.22-31
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• 2021

Surface image velocimetry (SIV) is a noncontact velocimetry technique based on images. Recently, studies have been conducted on surface velocity measurements using drones to measure a wide range of velocities and discharges. However, when measuring the surface velocity using a drone, reference points must be included in the image for image correction and the calculation of the ground sample distance, which limits the flight altitude and shooting area of the drone. A technique for calculating the surface velocity that does not require reference points must be developed to maximize spatial freedom, which is the advantage of velocity measurements using drone images. In this study, a technique for calculating the surface velocity that uses only the drone position and the specifications of the drone-mounted camera, without reference points, was developed. To verify the developed surface velocity calculation technique, surface velocities were calculated at the Andong River Experiment Center and then measured with a FlowTracker. The surface velocities measured by conventional SIV using reference points and those calculated by the developed SIV method without reference points were compared. The results confirmed an average difference of approximately 4.70% from the velocity obtained by the conventional SIV and approximately 4.60% from the velocity measured by FlowTracker. The proposed technique can accurately measure the surface velocity using a drone regardless of the flight altitude, shooting area, and analysis area.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.4
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• pp.117-122
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• 2021

In recent years, human damage and loss of money due to various disasters such as typhoons, earthquakes, forest fires, landslides, and wars are steadily occurring, and a lot of manpower and funds are required to prevent and recover them. In this paper, we designed and developed a disaster drone system based on artificial intelligence in order to monitor these various disaster situations in advance and to quickly recognize and respond to disaster occurrence. In this study, multiple disaster drones are used in areas where it is difficult for humans to monitor, and each drone performs an efficient search with an optimal path by applying a deep learning-based optimal path algorithm. In addition, in order to solve the problem of insufficient battery capacity, which is a fundamental problem of drones, the optimal route of each drone is determined using Ant Colony Optimization (ACO) technology. In order to implement the proposed system, it was applied to a forest fire situation among various disaster situations, and a forest fire map was created based on the transmitted data, and a forest fire map was visually shown to the fire fighters dispatched by a drone equipped with a beam projector. In the proposed system, multiple drones can detect a disaster situation in a short time by simultaneously performing optimal path search and object recognition. Based on this research, it can be used to build disaster drone infrastructure, search for victims (sea, mountain, jungle), self-extinguishing fire using drones, and security drones.

• 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.

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.69-80
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• 2022

Drone photogrammetry generally acquires images vertically or obliquely from above, so when photographing for the purpose of three-dimensional modeling, image matching for the ground of a building and spatial accuracy of point cloud data are poor, resulting in poor 3D mesh completeness. Therefore, to overcome this, this study analyzed the spatial accuracy of each drone image by acquiring smartphone images from the ground, and evaluated the accuracy improvement and completeness of 3D mesh when the smartphone image is not combined with the drone image. As a result of the study, the horizontal (x,y) accuracy of drone photogrammetry was about 1/200,000, similar to that of traditional photogrammetry. In addition, it was analyzed that the accuracy according to the photographing method was more affected by the photographing angle of the object than the increase in the number of photos. In the case of the smartphone image combination, the accuracy was not significantly affected, but the completeness of the 3D mesh was able to obtain a 3D mesh of about LoD3 that satisfies the digital twin city standard. Therefore, it is judged that it can be sufficiently used to build a 3D model for digital twin city by combining drone images and smartphones or DSLR images taken on the ground.