• Journal of Practical Engineering Education
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• v.9 no.1
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• pp.15-21
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• 2017

In order to satisfy the graduation requirements, the graduation work should be presented as an engineering dissertation system of the produced work and an outline of the procedure made by the major unit in accordance with the graduation thesis submission rules, and suggests necessary matters for improvement. The design content relates to a small unmanned aerial vehicle configuration for detecting personnel or objects in a disaster area. It is equipped with an infrared sensor and a GPS in the drone, the drone is control by using Blutooth communications. The drones detect the target and use the GPS to determine the location. As a result of the experiment, it was possible to detect the structure object within the range of 3~4 m, confirm the transmission of the position value in real time, and increase the communication distance by using RF communication.

• Journal of the Society of Disaster Information
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• v.15 no.1
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• pp.49-66
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• 2019

Purpose: This study aims to apply the counter terrorism policy in pertain to malicious drone abuse and the croweded public places in South Korea. And, to improve counter terrorism protection measure, this study suggests an adoptation of Anti UAV technology into counter terrorism related regulation. Method: Compared to nations' operations of counter terrorism prevention and protection activities with the South Korean gov, problems and limitations are suggested. Results: Anti UAV technology could not be applied for Multi-user facilities by any law due to the limitation, so that it is required to amend counte terrporism related policies and law. Conclusion: This study intends to identify various protection methods against UAV threats. To reduce the risk of UAV, the law of public safety and counter terrorism should be promoted and reinforced for the first.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.69-77
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• 2022

The 3D spatial model is an analysis framework for solving urban problems and is used in various fields such as urban planning, environment, land and housing management, and disaster simulation. The utilization of drones that can capture 3D images in a short time at a low cost is increasing for the construction of 3D spatial model. In terms of building a virtual city and utilizing simulation modules, high location accuracy of aerial survey and precision of 3D spatial model function as important factors, so a method to increase the accuracy has been proposed. This study analyzed location accuracy of aerial survey and precision of 3D spatial model by each condition of aerial survey for urban areas where buildings are densely located. We selected Daerim 2-dong, Yeongdeungpo-gu, Seoul as a target area and applied shooting angle, shooting altitude, and overlap rate as conditions for the aerial survey. In this study, we calculated the location accuracy of aerial survey by analyzing the difference between an actual survey value of CPs and a predicted value of 3D spatial Model. Also, We calculated the precision of 3D spatial Model by analyzing the difference between the position of Point cloud and the 3D spatial Model (3D Mesh). As a result of this study, the location accuracy tended to be high at a relatively high rate of overlap, but the higher the rate of overlap, the lower the precision of 3D spatial model and the higher the shooting angle, the higher precision. Also, there was no significant relationship with precision. In terms of baseline-height ratio, the precision tended to be improved as the baseline-height ratio increased.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.155-163
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• 2019

This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.