• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1385-1386
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• 2015

본 논문은 실내 실험 환경에서 카메라에서 얻어진 영상정보를 캐니 경계선 검출 알고리즘을 적용하여 정지 상태인 미니 드론의 경계선을 검출하고 이를 기반으로 좌표를 인식한다. 캐니 알고리즘의 임계값에 따른 검출 결과의 변화를 확인한다.

• Smart Media Journal
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• v.7 no.4
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• pp.44-51
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• 2018

The purpose of this research is to collect atmospheric environmental information at specific altitudes in a range of 0 to 1 km above the surface and to monitor it using drones. The corresponding temperature and humidity were measured with the meteorological factors, and the amounts of fine dust and $CO_2$ were observed by the environmental factors so that they could receive the normal values. Monitoring the status of atmospheric gas emission in specific enterprises, industrial complexes and regions through the measurement is meant to help establish policies to reduce pollution factors. In conventional means previously practiced, exhaust gas detection accompanies a great deal of risks in terms of safety because the surveyor is directly exposed to the source of contamination such as the holes installed in the chimney. However, in our proposed method, the drone can collect information in a wide range under safe circumstances, which can be utilized through wide industrial areas.

• Journal of Practical Engineering Education
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• v.9 no.1
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• pp.7-13
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• 2017

This study will provide a practical plan of engineering education through the study of major activities connected with the production of works to accomplish the graduation conditions by completing the comprehensive design subject and the result of the performance. The designed subject is to measure the minimum safety distance during driving using the obstacle detection function of the ultrasonic sensor and to perform the avoidance algorithm based on the measurement value of the acceleration gyro sensor. It is proposed an access surveillance system that minimizes the damage of drones, surrounding objects, and people, and improves air mobility. Experimental results show that the obstacles around the drone are detected by five ultrasonic sensors and the difference of output value is applied to each motor of the drone and obstacle avoidance is confirmed. In addition, the content and level of the data for measuring the achievement of learning achievement in the engineering education certification program were used and the results were confirmed to be consistent with the description of the engineering problem level required for the graduates of 4-year engineering college.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.187-192
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• 2021

This study proposes a system for take-off in a forest or similar complex environment using an object detector. In the simulator, a raspberry pi is mounted on a quadcopter with a length of 550mm between motors on a diagonal line, and the experiment is conducted based on edge computing. As for the images to be used for learning, about 150 images of 640⁎480 size were obtained by selecting three points inside Kunsan University, and then converting them to black and white, and pre-processing the binarization by placing a boundary value of 127. After that, we trained the SSD_Inception model. In the simulation, as a result of the experiment of taking off the drone through the model trained with the verification image as an input, a trajectory similar to the takeoff was drawn using the label.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.31-37
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• 2020

Last year's Goseong-Sokcho forest fires have highlighted the limitations of extinguishing work for night-time forest fire and the importance of quick identification for information on the spread of forest fire. However, it is not easy to find services that take into account the characteristics of forest fires, as most existing disaster-related mobile applications and research assume various disaster situations rather than just forest fire situations. Therefore, a system that can provide information quickly is needed, taking into account the characteristics of forest fires and the limitations of extinguishing work. In this paper, we propose evacuation route guidance services that bypass areas where fire has already spread, supplement existing methods of extinguishing work, and provide general information on forest fire situations in real time, by putting drones into forest fire situations. It has been implemented to automate image analysis using the Rekognition service of Amazon Web Service (AWS), and the results of fire detection and the T Map API guide the evacuation path. It is expected that the results of this paper will allow efficient and rapid rescue and extinguishing work to be carried out, and further reduce the damage of human life caused by forest fires.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.145-156
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• 2022

Nowadays, the complexity of object tracking models among hardware applications has become a more in-demand duty to complete in various indeterminable environment tracking situations with multifunctional algorithm skills. In this paper, we propose a virtual city environment using AirSim (Aerial Informatics and Robotics Simulation - AirSim, CityEnvironment) and use the DQN (Deep Q-Learning) model of deep reinforcement learning model in the virtual environment. The proposed object tracking DQN network observes the environment using a deep reinforcement learning model that receives continuous images taken by a virtual environment simulation system as input to control the operation of a virtual drone. The deep reinforcement learning model is pre-trained using various existing continuous image sets. Since the existing various continuous image sets are image data of real environments and objects, it is implemented in 3D to track virtual environments and moving objects in them.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.249-257
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• 2024

This study analyzes the current status and prospects of Iran's air defense network, focusing on the Russian-made S-300 system, and derives implications for the development of South Korea's air defense network. Iran's air defense network exhibits strengths such as long-range detection and interception capabilities, multi-target processing, high-altitude interception, and electronic warfare response. However, it also reveals weaknesses, including lack of mobility, difficulty in detecting low-altitude targets, obsolescence, training level of operating personnel, and vulnerability to electronic warfare. Real-world cases confirm these weaknesses, making the system susceptible to enemy evasion tactics, swarm drone attacks, and electronic warfare. Drawing from Iran's case, South Korea should establish a multi-layered defense system, strengthen low-altitude air defense and electronic warfare capabilities, foster the domestic defense industry for technological self-reliance, and enhance international cooperation. By addressing these aspects, South Korea can establish a robust air defense network and firmly protect its national security. Future research should aim to secure and analyze materials from the Iranian perspective for a more objective evaluation of Iran's air defense network and continuously track Iran's efforts to improve its air defense network and the trend of strengthening drone forces to predict changes in the Middle East security situation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.107-112
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• 2020

Recently, as the field of use of drones is diversified, it is actively used not only for surveying but also for search and rescue work. In these applications it is very important to know the location of the target or the location of the UAV. This paper proposes a target detection method using images taken from drones. The proposed method calculates the latitude and longitude information of the target by finding the location of the target by comparing it with the image to find the image taken by the drone. The exact latitude and longitude information of the target is calculated by calculating the actual distance corresponding to the distance of the image image using the characteristics of the pinhole camera. The proposed method through the actual experiment confirmed that the latitude and longitude of the target was accurately identified.

• Korean Journal of Construction Engineering and Management
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• v.23 no.2
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• pp.45-53
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• 2022

The purpose of this study is to present a model for analyzing construction errors of reinforced concrete pile foundations using drones. First, a drone is used to obtain an aerial image of the construction site, and an orthomosaic image is generated based on those images. Then, the circular pile foundation is automatically recognized from the orthomosaic image by using the Hough transform circle detection method. Finally, the distance is calculated based on the the center point of the reinforced concrete pile foundation in the overlapped data. As a case study, the proposed concrete concrete pile foundation construction quality control model was applied to the real construction site in Incheon to evaluate the proposed model.

• Smart Media Journal
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• v.12 no.9
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• pp.159-170
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• 2023

It takes a lot of time and manpower to search for the missing. As part of the solution, a missing person search AI system was implemented using a YOLO-based model. In order to train object detection models, the model was learned by collecting recognition images (road fixation) of drone mobile objects from AI-Hub. Additional mountainous terrain datasets were also collected to evaluate performance in training datasets and other environments. In order to optimize the missing person search AI system, performance evaluation based on model size and hyperparameters and additional performance evaluation for concerns about overfitting were conducted. As a result of performance evaluation, it was confirmed that the YOLOv5-L model showed excellent performance, and the performance of the model was further improved by applying data augmentation techniques. Since then, the web service has been applied with the YOLOv5-L model that applies data augmentation techniques to increase the efficiency of searching for missing people.