• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1648-1672
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• 2020

There are several types of image registration in the sense of stitching separated images that overlap each other. One of these is feature-based registration by a common feature descriptor. In this study, we generate a mosaic of images using feature-based registration for drone aerial images. As a feature descriptor, we apply the scale-invariant feature transform descriptor. In order to investigate the authenticity of the feature points and to have the mapping function, we employ the sample consensus method; we consider the sensed image's inherent characteristic such as the geometric congruence between the feature points of the images to propose a novel hypothesis estimation of the mapping function of the stitching via some optimally chosen initial candidate inliers in the sample consensus method. Based on the experimental results, we show the efficiency of the proposed method compared with benchmark methodologies of random sampling consensus method (RANSAC); the well-ordering property defined in the context and the extensive stitching examples have supported the utility. Moreover, the sample consensus scheme proposed in this study is uncomplicated and robust, and some fatal miss stitching by RANSAC is remarkably reduced in the measure of the pixel difference.

• 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.

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

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.639-644
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• 2024

Recent developments in the use of drones on battlefields, extending beyond reconnaissance to firepower support, have greatly increased the importance of technologies for early automatic drone identification. In this study, to identify an effective image classification model that can distinguish drones from other aerial targets of similar size and appearance, such as birds and balloons, we utilized a dataset of 3,600 images collected from the internet. We adopted a transfer learning approach that combines the feature extraction capabilities of three pre-trained convolutional neural network models (VGG16, ResNet50, InceptionV3) with an additional classifier. Specifically, we conducted a comparative analysis of the performance of these three pre-trained models to determine the most effective one. The results showed that the InceptionV3 model achieved the highest accuracy at 99.66%. This research represents a new endeavor in utilizing existing convolutional neural network models and transfer learning for drone identification, which is expected to make a significant contribution to the advancement of drone identification technologies.

• Explosives and Blasting
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• v.38 no.3
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• pp.1-14
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• 2020

Stereophotogrammetry can be used for accurate and fast investigation of over-break or under-break which may form during the blasting of underground space. When integrated with small unmanned aerial vehicles(UAVs) or drones, stereophotogrammetry can be performed much more efficiently. However, since previous research are mostly focused on surface environments, underground applications of drone-based stereophotogrammetry are limited and rare. In order to expand the use of drone-based stereophotogrammetry in underground environments, this study investigated a rock surface of a underground mine through drone-based stereophotogrammetry. The accuracy of the investigation was evaluated and analyzed, which proved the method to be accurate in underground environments. Also, recommendations were proposed for the image acquisition and matching conditions for accurate and efficient application of drone-based stereophotogrammetry in underground environments.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.33-38
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• 2016

Understanding of current features of collapsed buildings, terrain, and other infrastructures is a critical issue for disaster site managers. On the other hand, a comprehensive site investigation of current location of survivors buried under the remains of a building is a difficult task for disaster managers due to the difficulties in acquiring the various information on the disaster sites. To overcome these circumstances, such as large disaster sites and limited capability of rescue workers, this study makes use of a drone (unmanned aerial vehicle) to effectively obtain current image data from large disaster areas. The framework of 3D model reconstruction of disaster sites using aerial imagery acquired by drones was also presented. The proposed methodology is expected to assist fire fighters and workers on disaster sites in making a rapid and accurate identification of the survivors under collapsed buildings.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.107-119
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• 2019

As the development of ICT and integration technology, many changes and innovations in agriculture field are implemented. The agricultural sector has shifted from a traditional industry to a new industrial form called the 6th industry combined with various advanced technologies such as ICT and IT. Various approaches have been attempted to analyze and predict crops based on spatial information. In particular, a variety of research has been carried out recently for crop cultivation and smart farms using drones. The goal of this study was to establish an agricultural drought monitoring system using drones to produce scientific and objective indicators of drought. A soil moisture sensor was installed in the drought area and checked the actual soil moisture. The soil moisture data was used by the reference value to compare and analyze the temperature and NDVI established by drones. The soil temperature by the drone thermal image sensor and the NDVI by the drone hyperspectral was analyzed the correlation between crop condition and soil moisture in study area. To verify this, the actual soil moisture was calculated using the soil moisture measurement sensor installed in the target area and compared with the drone performance. This study using drone drought monitoring system may enhance to promote the crop data and to save time and economy.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.119-125
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• 2017

The mud flat in Korea is the geographical feature generated from the sediment of rivers of Korea and China and it is the important topography for pollution purification and fishing industry. The mud flat is difficult to access such that it requires the aerial survey for the high-resolution spatial information of the area. In this study we used drones instead of the conventional aerial and remote sensing approaches which have shortcomings of costs and revisit times. We carried out GPS-based control point survey, temporal image acquisition using drones, bundle adjustment, stereo image processing for DSM and ortho photo generation, followed by co-registration between the spatio-temporal information.