• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.3
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• pp.127-134
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• 2018

Electric transmission towers are facilities to transport electrical power from a plant to an electrical substation. The towers are connected using power lines that are installed with a proper sag by loosening the cable to lower the tension and to secure the sufficient clearance from the ground or nearby objects. The power line sag may extend over the tolerance due to the weather such as strong winds, temperature changes, and a heavy snowfall. Therefore the periodical mapping of the power lines is required but the poor accessibility to the power lines limit the work because most power lines are placed at the mountain area. In addition, the manual mapping of the power lines is also time-consuming either using the terrestrial surveying or the aerial surveying. Therefore we utilized multiple overlapping images acquired from a low-cost drone to automatically reconstruct the power lines in the object space. Two overlapping images are selected for epipolar image resampling, followed by the line extraction for the resampled images and the redundant images. The extracted lines from the epipolar images are matched together and reconstructed for the power lines primitive that are noisy because of the multiple line matches. They are filtered using the extracted line information from the redundant images for final power lines points. The experiment result showed that the proposed method successfully generated parabolic curves of power lines by interpolating the power lines points though the line extraction and reconstruction were not complete in some part due to the lack of the image contrast.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.12
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• pp.76-84
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• 1999

This paper presents an integrated method for aircraft position estimation using sequential aerial images. The proposed integrated system for position estimation is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current position of an aircraft by accumulating relative displacement estimates extracted from two successive aerial images. Simple accumulation of parameter values decreases reliability of the extracted parameter estimates as an aircraft goes on navigating, resulting in large position error. Therefore absolute position estimation is required to compensate for the position error generated in relative position estimation. Absolute position estimation algorithms by image matching or digital elevation model (DEM) matching are presented. In image matching, a robust oriented Hausdorff measure (ROHM) is employed whereas in DEM matching an algorithm using multiple image pairs is used. Computer simulation with four real aerial image sequences shows the effectiveness of the proposed integrated position estimation algorithm.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.87-97
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• 2023

Unmanned aerial vehicles (UAVs) can capture high-resolution imagery from a variety of viewing angles and altitudes; they are generally limited to collecting images of small scenes from larger regions. To improve the utility of UAV-appropriated datasetsfor use with deep learning applications, multiple datasets created from variousregions under different conditions are needed. To demonstrate a powerful new method for integrating heterogeneous UAV datasets, this paper applies a combined segmentation network (CSN) to share UAVid and semantic drone dataset encoding blocks to learn their general features, whereas its decoding blocks are trained separately on each dataset. Experimental results show that our CSN improves the accuracy of specific classes (e.g., cars), which currently comprise a low ratio in both datasets. From this result, it is expected that the range of UAV dataset utilization will increase.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.125-132
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• 2023

Roundabouts have strengths in traffic flow and safety but can present difficulties for inexperienced drivers. Demand to acquire and analyze drone images has increased to enhance a traffic environment allowing drivers to deal with roundabouts easily. In this paper, we propose a roundabout traffic analysis system that detects, tracks, and analyzes vehicles using a deep learning-based object detection model (YOLOv7) in drone images. About 3600 images for object detection model learning and testing were extracted and labeled from 1 hour of drone video. Through training diverse conditions and evaluating the performance of object detection models, we achieved an average precision (AP) of up to 97.2%. In addition, we utilized SORT (Simple Online and Realtime Tracking) and OC-SORT (Observation-Centric SORT), a real-time object tracking algorithm, which resulted in an average MOTA (Multiple Object Tracking Accuracy) of up to 89.2%. By implementing a method for measuring roundabout entry speed, we achieved an accuracy of 94.5%.

• Smart Structures and Systems
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• v.25 no.6
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• pp.733-749
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• 2020

This study presents a computer vision-based approach for representing time evolution of structural damages leveraging a database of inspection images. Spatially incoherent but temporally sorted archival images captured by robotic cameras are exploited to represent the damage evolution over a long period of time. An access to a sequence of time-stamped inspection data recording the damage growth dynamics is premised to this end. Identification of a structural defect in the most recent inspection data set triggers an exhaustive search into the images collected during the previous inspections looking for correspondences based on spatial proximity. This is followed by a view synthesis from multiple candidate images resulting in a single reconstruction for each inspection round. Cracks on concrete surface are used as a case study to demonstrate the feasibility of this approach. Once the chronology is established, the damage severity is quantified at various levels of time scale documenting its progression through time. The proposed scheme enables the prediction of damage severity at a future point in time providing a scope for preemptive measures against imminent structural failure. On the whole, it is believed that the present study will immensely benefit the structural inspectors by introducing the time dimension into the autonomous condition assessment pipeline.

• Smart Structures and Systems
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• v.22 no.2
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• pp.201-207
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• 2018

This paper proposes a line laser thermography scanning (LLTS) system for multiple crack evaluation on a concrete structure, as the core technology for unmanned aerial vehicle-mounted crack inspection. The LLTS system consists of a line shape continuous-wave laser source, an infrared (IR) camera, a control computer and a scanning jig. The line laser generates thermal waves on a target concrete structure, and the IR camera simultaneously measures the corresponding thermal responses. By spatially scanning the LLTS system along a target concrete structure, multiple cracks even in a large scale concrete structure can be effectively visualized and evaluated. Since raw IR data obtained by scanning the LLTS system, however, includes timely- and spatially-varying IR images due to the limited field of view (FOV) of the LLTS system, a novel time-spatial-integrated (TSI) coordinate transform algorithm is developed for precise crack evaluation in a static condition. The proposed system has the following technical advantages: (1) the thermal wave propagation is effectively induced on a concrete structure with low thermal conductivity of approximately 0.8 W/m K; (2) the limited FOV issues can be solved by the TSI coordinate transform; and (3) multiple cracks are able to be visualized and evaluated by normalizing the responses based on phase mapping and spatial derivative processes. The proposed LLTS system is experimentally validated using a concrete specimen with various cracks. The experimental results reveal that the LLTS system successfully visualizes and evaluates multiple cracks without false alarms.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.1
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• pp.71-79
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• 2003

This study intended to extract the spatial data and attribute data from the images of terrestrial and aerial photographs and to compile the digital map from the images using various kinds of photograrmmetric analysis. Also this study aimed to develope the technique of constructing the Information System for Building and Road Facility (ISBRF) using the compiled digital maps and the extracted data. The spatial or attribute data for the facilities in the area was extracted using the single terrestial photographs and graphical method. And the Three Dimensional Frame Model was produced from multiple images of terrestial photographs. In addition, the spatial data base for the objective area was updated by the vectorizing procedures with small scale areal photos. It is concluded that the efficient techniques for constructing the ISBRF was suggested in this study.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.457-464
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• 2014

Recently, interests in space information data are increasing due to the initiation of spatial information open platform service by the Ministry of Land, Infrastructure and Transport. The purpose of this study is optimizing management and storing of the texture data, one kinds of 3D-spatial data. First, extract 3D-spatial data through the aerial triangulation and 3D-writing using raw image taken with the Multi-directional aerial camera and the vertical aerial camera. And develop the method to create single texture data and related technique by align and place corresponding 3D-spatial data to optimal storage space. Through experiment, the results show effect of 8 times of storage capacity reduction compared to existing single-file storage method, additionally, new method can improve file management efficiency in comparison with multiple file storage method. The results of this study can be cornerstone of three-dimensional space information management when dealing with bulk data, and utilizations will be enhanced through the further studies and algorithm improvement.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.445-446
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• 2018

최근 무인항공기 (Unmanned Aerial Vehicle, UAV)에 장착한 카메라를 활용하여 사용자의 눈높이가 아닌 새로운 시각에서 사용자를 촬영한 영상을 제공한다. 사용자를 추적하며 촬영하기 위해 저전력 블루투스 (Bluetooth Low Energy, BLE) 신호, 영상, 그리고 Natural User Interface/Natual User Experience(NUI/NUX) 기술을 활용한다. BLE 신호로 사용자를 추적하는 경우 사용자의 후방에서 추적하며 사용자만을 추적하며 촬영 가능한 문제가 있다. 하지만 복수의 사용자를 전방에서 추적하며 촬영하는 방법이 필요하다. 본 논문에서는 복수의 사용자를 추적하며 전방에서 촬영하기 위해 UAV의 비행방향을 결정하는 방법을 설명한다. 복수의 사용자로부터 측정 가능한 BLE 신호들을 UAV에서 측정한다. 복수개의 BLE 신호의 변화를 활용하여 UAV의 비행방향을 결정한다.