• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.103-103
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• 2023

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.217-226
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• 2022

In the view of the application of high-precision spatial information production, UAV (Umanned Aerial Vehicle)-Photogrammetry has a problem in that it lacks specific procedures and detailed regulations for quantitative quality verification methods or certification of captured images. In addition, test tools for UAV image quality assessment use only the GSD (Ground Sample Distance), not MTF (Modulation Transfer Function), which reflects image resolution and contrast at the same time. This fact makes often the quality of UAV image inferior to that of manned aerial image. We performed MTF and GSD analysis simultaneously using a siemens star to confirm the necessity of MTF analysis in UAV image quality assessment. The analyzing results of UAV images taken with different payload and sensors show that there is a big difference in σ_MTF values, representing image resolution and the degree of contrast, but slightly different in GSD. It concluded that the MTF analysis is a more objective and reliable analysis method than just the GSD analysis method, and high-quality drone images can only be obtained when the operator make images after judging the proper selection the sensor performance, image overlaps, and payload type. However, the results of this study are derived from analyzing only images acquired by limited sensors and imaging conditions. It is therefore expected that more objective and reliable results will be obtained if continuous research is conducted by accumulating various experimental data in related fields in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.372-378
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• 2020

Drones, which show strong growth in the fourth industry, are commonly used for disaster response. In the event of an actual disaster, local governments should carry out 13 cooperative functions to respond to the disaster, but there are difficulties in collecting on-site information in places where it is difficult for people to access or grasp the situation. Based on the 13 collaborative functions that are essential for operations in the event of a disaster, a utilization plan of highly utilized drones was derived. Through the analysis of overseas cases and drone utilization by each department, a total of 10 out of 13 collaborative functions of the Disaster and Safety Countermeasures Headquarters (disaster situation management, emergency life safety support, emergency recovery, disaster resource support, traffic measures, medical and quarantine services, disaster site environment maintenance, social order maintenance, search, rescue, and emergency) were derived. These results can enhance the efficiency of the disaster response by presenting a plan to utilize drone images for each function.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.63-71
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• 2021

Among drone autonomous flight technologies, obstacle avoidance is a very important technology that can prevent damage to drones or surrounding environments and prevent danger. Although the LiDAR sensor-based obstacle avoidance method shows relatively high accuracy and is widely used in recent studies, it has disadvantages of high unit price and limited processing capacity for visual information. Therefore, this paper proposes an obstacle avoidance algorithm for drones using camera-based PPO(Proximal Policy Optimization) reinforcement learning, which is relatively inexpensive and highly scalable using visual information. Drone, obstacles, target points, etc. are randomly located in a learning environment in the three-dimensional space, stereo images are obtained using a Unity camera, and then YOLov4Tiny object detection is performed. Next, the distance between the drone and the detected object is measured through triangulation of the stereo camera. Based on this distance, the presence or absence of obstacles is determined. Penalties are set if they are obstacles and rewards are given if they are target points. The experimennt of this method shows that a camera-based obstacle avoidance algorithm can be a sufficiently similar level of accuracy and average target point arrival time compared to a LiDAR-based obstacle avoidance algorithm, so it is highly likely to be used.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.30-39
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• 2023

Large dams, which are critical infrastructures for disaster prevention, are exposed to various risks such as aging, floods, and earthquakes. Better dam safety inspection and diagnosis using digital transformation technologies are needed. Traditional visual inspection methods by human inspectors have several limitations, including many inaccessible areas, danger of working at heights, and know-how based subjective inspections. In this study, drone photogrammetry was performed on two large dams to evaluate the applicability of digital data-based dam safety inspection and propose a data management methodology for continuous use. High-quality 3D digital models with GSD (ground sampling distance) within 2.5 cm/pixel were generated by flat double grid missions and manual photography methods, despite reservoir water surface and electromagnetic interferences, and severe altitude differences ranging from 42 m to 99.9 m of dam heights. Geometry profiles of the as-built conditions were easily extracted from the generated 3D mesh models, orthomosaic images, and digital surface models. The effectiveness of monitoring dam deformation by photogrammetry was confirmed. Cracks and deterioration of dam concrete structures, such as spillways and intake towers, were detected and visualized efficiently using the digital 3D models. This can be used for safe inspection of inaccessible areas and avoiding risky tasks at heights. Furthermore, a methodology for mapping the inspection result onto the 3D digital model and structuring a relational database for managing deterioration information history was proposed. As a result of measuring the labor and time required for safety inspection at the SYG Dam spillway, the drone photogrammetry method was found to have a 48% productivity improvement effect compared to the conventional manpower visual inspection method. The drone photogrammetry-based dam safety inspection is considered very effective in improving work productivity and data reliability.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.2_2
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• pp.255-261
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• 2020

We consider the problem of autonomously flying a quadcopter in indoor environments. Navigation in indoor settings poses two major issues. First, real time recognition of the marker captured by the camera. Second, The combination of the distributed images is used to determine the position and orientation of the quadcopter in an indoor environment. We autonomously fly a miniature RC quadcopter in small known environments using an on-board camera as the only sensor. We use an algorithm that combines data-driven image classification with image-combine techniques on the images captured by the camera to achieve real 3D localization and navigation.

• Journal of the Korean Institute of Gas
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• v.25 no.5
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• pp.88-96
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• 2021

Advanced Internet of Things (IoT) technology is being used in various ways for the safety of the energy industry. At the center of safety measures, drones play various roles on behalf of humans. Drones are playing a role in reaching places that are difficult to reach due to large-scale facilities and space restrictions that are difficult for humans to inspect. In this study, the accuracy and completeness of movement of dangerous facilities were tested using drone images, and it was confirmed that the movement recognition accuracy was 100%, the average data analysis accuracy was 95.8699%, and the average completeness was 100%. Based on the experimental results, a future-oriented facility risk analysis system combined with ICT technology was implemented and presented. Additional experiments with diversified conditions are required in the future, and ICT convergence analysis system implementation is required.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.251-256
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• 2019

With the fast development of modern society, the interests concerned about the significance of nature and environment become major issue nowadays. Especially, threats for our health due to severe environmental pollution and fine dusts become serious problem with the fast industrialization of our society, and extra attention is focused on interests about conservation of nature and management of forest resources. Precious forest resources, however, are not properly managed and destroyed vainly due to frequent fire, damage by storms and floods, and unplanned land development. So systematic and scientific construction and management of forest resources are required in order to solve these problems efficiently. Furthermore, implementation of the forest resource information database that contains information of trees, Topography, ecosystem of the forest is urgently needed. In this paper, we design and implement the forest resource information database based on the information of location based forest resources and Topography using forest images taken by a drone, that enables us to manage forest resources efficiently, make decision for logging, and construct a future tree-planting project easily.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.7-12
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• 2019

In order to shoot underwater, the photographer must be equipped with shooting equipment and enter into the water. Since the photographer directly enters the water, safety accidents occur frequently due to various obstacles or deep water in the water. The proposed underwater stereo photography technique can solve the safety accident problem caused by the entry of the photographer into the water by using the drone for underwater photographing. In addition, this technique has the advantage of obtaining underwater images at low cost. In this study, the angle of the proposed cam for stereoscopic photography was analyzed and the condition that the proper stereoscopic image can be viewed was defined as the distance from the floor of 18cm to the floor distance of 41.4cm. This provision is proposed to be used to adjust the height of the shooting area descended by the elevation chain of the water surface hovering drones.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.33-40
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• 2022

In this paper, we propose a system that analyzes drone photographic images of panel-type factory roofs and conducts abnormal detection of bolts. Currently, inspectors directly climb onto the roof to carry out the inspection. However, safety accidents caused by working conditions at high places are continuously occurring, and new alternatives are needed. In response, the results of drone photography, which has recently emerged as an alternative to the dangerous environment inspection plan, will be easily inspected by finding the location of abnormal bolts using deep learning. The system proposed in this study proceeds with scanning the captured drone image using a sample image for the situation where the bolt cap is released. Furthermore, the scanned position is discriminated by using AI, and the presence/absence of the bolt abnormality is accurately discriminated. The AI used in this study showed 99% accuracy in test results based on VGGNet.