• 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 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.

• Journal of Advanced Navigation Technology
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• v.8 no.1
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• pp.19-26
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• 2004

This paper deals with the automatic flight control system for an unmanned target drone which is operated by an army as an anti-air gun shooting training. By automation of unmanned target drone that is manually operated by external pilot, pilot can reduce workload and an army can reduce the budget. Most UAVs which are developed until today use high-cost sensors as AHRS and IMU to measure the attitude, but those are contradictory for the reduction of budget. This paper says the development of low-cost automatic flight control system which makes possible of automatic flight with low-cost sensors. We have developed the integrated automatic flight control system by integrating electricity module, switching module, monitoring module and RC receiver as an one module. We also prove the performance of automatic flight control system by flight test.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.47-54
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• 2021

Calculation of quantity at construction sites is a factor that has a great influence on construction costs, and it is important to calculate accurate values. In this study, topographic model was created by using drone photogrammetry and drone LiDAR to estimate earthwork volume. ortho image and DSM (Digital Surface Model) were constructed for the study area by drone photogrammetry, and DEM (Digital Elevation Model) of the target area was established using drone LiDAR. And through accuracy evaluation, accuracy of each method are 0.034m, 0.35m in horizontal direction, 0.054m, 0.25m in vertical direction. Through the research, the usability of drone photogrammetry and drone LiDAR for constructing geospatial information was presented. As a result of calculating the volume of the study site, the UAV photogrammetry showed a difference of 1528.1㎥ from the GNSS (Global Navigation Satellite System) survey performance, and the 3D Laser Scanner showed difference of 160.28㎥. The difference in the volume of earthwork is due to the difference in the topographic model, and the efficiency of volume calculation by drone LiDAR could be suggested. In the future, if additional research is conducted using GNSS surveying and drone LiDAR to establish topographic model in the forest area and evaluate its usability, the efficiency of terrain model construction using drone LiDAR can be suggested.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.249-256
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• 2022

In the case of Drone Photogrammetry, the "pixel to point tool" module of Metashape, Pix4D Mapper, ContextCapture, and Global MapperGIS, which is a simple software, are widely used. Each SW has its own logic for the analysis of aerial triangulation, but from the user's point of view, it is necessary to select a SW by comparative analysis of the coordinate values of geospatial information for the result. Taking aerial photos for drone photogrammetry, surveying GCP reference points through VRS-GPS Survey, processing the acquired basic data using each SW to construct ortho image and DSM, and GCPSurvey performance and acquisition from each SW The coordinates (X,Y) of the center point of the GCP target on the Ortho-Image and the height value (EL) of the GCP point by DSM were compared. According to the "Public Surveying Work Regulations", the results of each SW are all within the margin of error. It turned out that there is no problem with the regulations no matter which SW is included within the scope.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.802-812
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• 2019

With the rapid development in the field of unmanned aerial vehicles(UAVs) and drones, higher request to development of a surveillance system for a drone is putting forward. Since surveillance systems with fixed cameras have a limited range, a development of surveillance systems with a moving camera applicable to PTZ(Pan-Tilt-Zoom) cameras is required. Selecting the features for object plays a critical role in tracking, and the object has to be represented by their shapes or appearances. Considering these conditions, in this paper, an object tracking method with optical flow is introduced to track a tiny drone with a moving camera. In addition, a tracking method combined with kalman filter is proposed to track continuously even when tracking is failed. Experiments are tested on sequences which have a target from the minimal 12 pixels to the maximal 56337 pixels, the proposed method achieves average precision of 175% improvement. Also, experimental results show the proposed method tracks a target which has a size of 12pixels.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.953-958
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• 2022

Holographic Stereogram can provide the depth perception without the visual fatigue and dizziness because it use multiple images acquired from the multiple viewpoints. In order to produce a holographic stereogram, it is necessary to obtain perspective images of a live object and record it on film using a digital hologram printer. when acquiring perspective images, the hologram without distortion can be produced only when the perspective images with a constant distance between the camera and the target is obtained. If the target is small, it is possible to keep the constant distance from the camera to object. but if it is large, this is difficult to keep the constant distance. In this study, we photograph the large object using the POI (Point of Interest) function which is one of the smart flight modes of drone to produce perspective images required for the hologram production. after that, problems such as the unexpected shakings and distance change between camera and object is corrected in post production. as a result, we produce the perspective images.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_2
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• pp.1073-1082
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• 2023

Thermal sensors, also called thermal infrared wavelength sensors, measure temperature based on the intensity of infrared signals that reach the sensor. The infrared signals recognized by the sensor include infrared wavelength(0.7~3.0㎛) and radiant infrared wavelength(3.0~100㎛). Infrared(IR) wavelengths are divided into five bands: near infrared(NIR), shortwave infrared(SWIR), midwave infrared(MWIR), longwave infrared(LWIR), and far infrared(FIR). Most thermal sensors use the LWIR to capture images. Thermal sensors measure the temperature of the target in a non-contact manner, and the data can be affected by the sensor's viewing angle between the target and the sensor, the amount of atmospheric water vapor (humidity), air temperature, and ground conditions. In this study, the characteristics of three thermal imaging sensor models that are widely used for observation using unmanned aerial vehicles were evaluated, and the optimal application field was determined.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.2
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• pp.189-196
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• 2024

Classification of drones and birds is challenging due to diverse flight patterns and limited data availability. Previous research has focused on identifying the flight patterns of unmanned aerial vehicles by emphasizing dynamic features such as speed and heading. However, this approach tends to neglect crucial spatial information, making accurate discrimination of unmanned aerial vehicle characteristics challenging. Furthermore, training methods for situations with imbalanced data among classes have not been proposed by traditional machine learning techniques. In this paper, we propose a data processing method that preserves angle information while maintaining positional details, enabling the deep learning model to better comprehend positional information of drones. Additionally, we introduce a training technique to address the issue of data imbalance.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.335-343
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• 2023

Drone based Magnetic Anomaly Detection measure a magnetic anomaly signal from the ferromagnetic target on the ground. We conduct a magnetic anomaly detection with 9 ferromagnetic targets on the ground. By removing the magnetic field measured in the absence of ferromagnetic targets from the experimental value, the magnetic anomaly signal is clearly measured at an altitude of 100 m. We analyze the signal characteristics by the ferromagnetic target through simulation using COMSOL multiphysics. The simulation results are within the GPS error range of the experimental results.