• Ecology and Resilient Infrastructure
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• v.6 no.3
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• pp.171-177
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• 2019

The hyperspectral images can be analyzed in more detail compared to the conventional multispectral images so they can be used for analyzing surface properties which are difficult to detect. Therefore, the purpose of this study is to obtain information on river environment by using actual depth data and drone-based images. For this purpose, this study acquired the image values for 100 points of 1 survey line using drone-based hyperspectral sensors and analyzed the correlation in comparison with the actual depth information obtained through ADCP. The ADCP measurements showed that the depth tended to get deeper toward the center and that the average water depth was 0.81 m. As a result of analyzing the hyperspectral images, the value of maximum intensity was 645 and the value of minimum intensity was 278, and the correlation between the actual depth and the results of analyzing the hyperspectral images showed that the depth increased as the value of maximum intensity decreased.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.536-541
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• 2015

In this paper, we propose a the image-based automatic flight control system for the mini drone. Automatic flight system with a camera on the ceiling and markers on the floor and landing position is designed in an indoor environment. Images from the ceiling camera is used not only to recognize the makers and landing position but also to track the drone motion. PC sever identifies the location of the drone and sends control commands to the mini drone. Flight controller of the mini drone is designed using state-machine algorithm, PID control and way-point position control method. From the, The proposed automatic flight control system is verified through the experiments of the mini drone. We see that known makers in environment are recognized and the drone can follows the trajectories with the specific ㄱ, ㄷ and ㅁ shapes. Also, experimental results show that the drone can approach and correctly land on the target positions which are set at different height.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1645-1652
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• 2018

A drone which was developed first for military purpose has been expanded to various civil areas, with its technological development. Of the drones developed for such diverse purposes, a drone for photography has a camera installed and is actively applied to a variety of image contents making, beyond filming and broadcasting. A drone for photography makes it possible to shoot present and dynamic images which were hard to be photographed with conventional photography technology. This study made a view finder which helps a drone operator to control a drone and directly view an object to shoot with the drone camera. The view finder for drones is a type of glasses. It was developed in the way of printing out the data modelled with 3D MAX in a 3D printer and installing a ultra-small LCD monitor. The view finder for drones makes it possible to fly a drone safely and achieve accurate framing of an object to shoot.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.1
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• pp.19-28
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• 2019

Images captured by drone have the advantage of quickly constructing spatial information in small areas and are applied to fields that require quick decision making. If an image registration technique that can automatically register the drone image on the ortho-image with the ground coordinate system is applied, it can be used for various analyses. In this study, a methodology for geo-referencing of a single image and sequential images using drones was proposed even if they differ in spatio-temporal resolution using linear features and distinct points. Through the method using linear features, projective transformation parameters for the initial geo-referencing between images were determined, and then finally the geo-referencing of the image was performed through the template matching for distinct points that can be extracted from the images. Experimental results showed that the accuracy of the geo-referencing was high in an area where relief displacement of the terrain was not large. On the other hand, there were some errors in the quantitative aspect of the area where the change of the terrain was large. However, it was considered that the results of geo-referencing of the sequential images could be fully utilized for the qualitative analysis.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.1
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• pp.121-131
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• 2023

Although the utilization of drone photogrammetry that can generate spatial information using ultra-high-resolution images is increasing in the civil engineering and construction fields, analysis of areas that can be used is insufficient. Therefore, this study attempted to determine how far drone photogrammetry can be used in the civil engineering and construction fields by applying a detailed analysis method. The status map and cross-sectional map were actually vectorized using drone photogrammetry outcomes, compared and analyzed with the results acquired in the field, and the qualitative aspects of traffic safety facilities were analyzed to determine their usability. As a result, the accuracy of the plane position using drone photogrammetry was reliable, but the height accuracy was difficult to trust. Accordingly, although there is a possibility of preparing a status map, the use of it in areas requiring high accuracy such as cross-sectional plans was limited, and it is believed that it can be used in the construction management field where qualitative analysis is conducted.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.2
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• pp.359-363
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• 2019

As commercial drones have been widely used, concerns for collision accidents with people and invading secured properties are emerging. The detection of drone is a challenging problem. The deep learning based object detection techniques for detecting drones have been applied, but limited to the specific cases such as detection of drones from bird and/or background. We have tried not only detection of drones, but classification of different drones with an end-to-end model. YOLOv2 is used as an object detection model. In order to supplement insufficient data by shooting drones, data augmentation from collected images is executed. Also transfer learning from ImageNet for YOLOv2 darknet framework is performed. The experimental results for drone detection with average IoU and recall are compared and analysed.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.3
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• pp.195-202
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• 2017

Ortho images created by aerial photogrammetry have been used in large areas but they are uneconomical for small areas and continuous change observation. The drones have been developed for military purposes, and recently they are being used crop management and analysis, broadcast relay, meteorological observation and disaster investigation and so on. Also there were a lot of studies of expensive commercial drone. In this paper, lower price self-assembly drone usable for in small areas, Obtained images and produced Orthomosaic and DSM using mission planner which is a normal digital camera and open source program, and postprocessing was used Pix4d software. GCP errors are X-coordinate 3.4cm, Y-coordinate 2.4cm, Z-coordinate 4.2cm. It seems like the self-assembly drone can be used for various fields.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.7
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• pp.1081-1087
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• 2015

Recently, the researches of aerial drones are actively executed in various areas, the researches of surface drones and underwater drones are also executed in marine areas. In case of surface drones, they essentially utilize acoustic information by the sonar and consequently have the local information in the obstacle avoidance as the sonar has the limitations due to the beam width and detection range. In order to overcome this, more global method that utilizes optical images by the camera is required. Related to this, the aerial drone with the camera is desirable as the obstacle detection of the surface drone with the camera is impossible in case of the existence of clutters. However, the dynamic-floating aerial drone is not desirable for the long-term operation as its power consumption is high. To solve this problem, a collaborative obstacle avoidance method based on the acoustic information by the sonar of the surface drone and the optical image by the camera of the static-floating aerial drone is proposed. To verify the performance of the proposed method, the collaborative obstacle avoidances of a MSD(Micro Surface Drone) with an OAS(Obstacle Avoidance Sonar) and a BMAD(Balloon-based Micro Aerial Drone) with a camera are executed. The test results show the possibility of real applications and the need for additional studies.

• Journal of Cadastre & Land InformatiX
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• v.51 no.1
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• pp.79-95
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• 2021

This study evaluates the applicability of determining cadastral surveying results using drone photogrammetry during the phase of determining cadastral surveying results, which is the most important stage of cadastral surveying, but known to be hardly objective and highly probable in causing a subjective misjudgment or mistake made by a surveyor. In the experiment to analyze the accuracy of boundary point extraction from drone photogrammetry results, by comparing the coordinate area of 22 parcels extracted from 2D and 3D images with the coordinate area measured from ground survey, the difference could be quantified as RMSE of 1.44m² for 2D and 0.32m² for 3D images. In addition, experiments to evaluate the determination of cadastral surveying result based on drone photogrammetry survey showed the RMSE measure of 0.346m towards N direction and 0.296m towards Y direction in comparison to the existing surveying results through data investigation. Based on these experiments, it is judged that cadastral surveying result based on drone photogrammetry can be determined without needing to conduct a location survey with an accuracy of approximately 0.3m in the graphical area, which also leads to possibility of reducing individual errors if drones images are used along with ground survey by objectifying the process of cadastral surveying results.