• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.351-357
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• 2019

UAV (Unmanned Aerial Vehicle) images can be exploited for rapid forest fire damage assessment by virtue of UAV systems' advantages. In 2019, catastrophic forest fire occurred in Goseong and Sokcho, Korea and burned 1,757 hectares of forests. We visited the town in Goseong where suffered the most severe damage and conducted UAV flights for forest fire damage assessment. In this study, economic and rapid damage assessment method for forest fire has been proposed using UAV systems equipped with only a RGB sensor. First, forest masking was performed using automatic elevation thresholding to extract forest area. Then ExG (Excess Green) vegetation index which can be calculated without near-infrared band was adopted to extract damaged forests. In addition, entropy filtering was applied to ExG for better differentiation between damaged and non-damaged forest. We could confirm that the proposed forest masking can screen out non-forest land covers such as bare soil, agriculture lands, and artificial objects. In addition, entropy filtering enhanced the ExG homogeneity difference between damaged and non-damaged forests. The automatically detected damaged forests of the proposed method showed high accuracy of 87%.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.397-406
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• 2022

Recently, research and development to revitalize smart construction are being actively carried out. Accordingly, 3D mapping technology that digitizes construction site is drawing attention. To create a 3D digital map for construction site a point cloud generation method based on LiDAR(Light detection and ranging) using MMS(Mobile mapping system) is mainly used. The purpose of this study is to analyze the accuracy of MMS LiDAR-based point cloud data. As a result, accuracy of MMS point cloud data was analyzed as dx = 0.048m, dy = 0.018m, dz = 0.045m on average. In future studies, accuracy comparison of point cloud data produced via UAV(Unmanned aerial vegicle) photogrammetry and MMS LiDAR should be studied.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.1
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• pp.29-38
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• 2014

UAV(Unmanned Aerial vehicle) could be effectively applied in a field of land monitoring for analyzing disaster area and mapping, because it can quickly acquire image data at low costs. For this reason, we reviewed the legal system related to mapping, and proposed suggestions for improving in legal system, due to introducing the UAV to Korean land-monitoring through this paper. Also, we evaluated spatial and time accuracy of the digital map, which are generated from UAV images that were taken for occasional map updates and disaster detections. As a result, the mean error is about 10m if only GPS/INS data used, while using GCP(Ground Control Points) it is about 10cm. Therefore, we conclude that the UAV could be effective method in korea land-monitoring field.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.81-89
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• 2017

This study investigates store separation characteristics of an unmanned aerial vehicle having generic stealth configuration over unsteady flow of an internal bay. Free-drop wind tunnel tests are conducted to simulate bomb releases from an internal weapons bay while high-speed camera images are taken. The images are analyzed to examine the effects of flow velocity, angle of attack, flap deflection and the ejector force application on the store separation trajectories. For the free-drop wind tunnel tests, Froude Scaling is applied to match the dynamic similarity for the bomb model, and the ejector force is simulated by using small pneumatic cylinders. The results indicate that the test bomb model safely separates from the internal bay at the given test conditions and configurations. It is also observed that the effects of the flow velocity and ejector force application have greater impacts on the separation trajectories than those of angle of attack and flap deflection.

• Journal of Cadastre & Land InformatiX
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• v.51 no.1
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• pp.111-124
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• 2021

Recently, Interest in the restoration and 3D reconstruction of cultural properties due to the fire of Notre Dame Cathedral on April 15, 2019 has been focused once again after the 2008 Sungnyemun fire incident in South Korea. In particular, research to restore and reconstruct the actual measurement of cultural properties using LiDAR(Light Detection and ranging) and conventional surveying, which were previously used, using various 3D reconstruction technologies, is being actively conducted. This study acquires data using unmanned aerial imagery of UAV(Unmanned Aerial Vehicle), which has recently established itself as a core technology in the era of the 4th industrial revolution, and the existing CRP(Closed Range Photogrammetry) and terrestrial LiDAR scanning for the Recumbent Buddha of Unju Temple. Then, the 3D reconstruction was performed with three fusion models based on SfM(Structure-from-Motion), and the reproducibility and accuracy of the models were compared and analyzed. In addition, using the best fusion model among the three models, the relationship with the Polar Star(Polaris) was confirmed based on the real world coordinates of the Recumbent Buddha, which contains the astronomical history of Buddhism in the early 11th century Goryeo Dynasty. Through this study, not only the simple external 3D reconstruction of cultural properties, but also the method of reconstructing the historical evidence according to the type and shape of the cultural properties was sought by confirming the historical evidence of the cultural properties in terms of spatial information.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.599-608
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• 2017

Cracks on the asphalt road surface can affect the speed of the car, the consumption of fuel, the ride quality of the road, and the durability of the road surface. Such cracks in roads can lead to very dangerous consequences for long periods of time. To prevent such risks, it is necessary to identify cracks and take appropriate action. It takes too much time and money to do it. Also, it is difficult to use expensive laser equipment vehicles for initial cost and equipment operation. In this paper, we propose an effective detection method of road surface defect using ROI (Region of Interest) setting and cany edge detection method using UAV image. The results of this study can be presented as efficient method for road surface flaw detection and maintenance using UAV. In addition, it can be used to detect cracks such as various buildings and civil engineering structures such as buildings, outer walls, large-scale storage tanks other than roads, and cost reduction effect can be expected.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.4
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• pp.263-270
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• 2018

Land surface temperature is known to be an important factor in understanding the interactions of the ground-atmosphere. However, because of the large spatio-temporal variability, regular observation is rarely made. The existing land surface temperature is observed using satellite images, but due to the nature of satellite, it has the limit of long revisit period and low accuracy. In this study, in order to confirm the possibility of replacing land surface temperature observation using satellite imagery, images acquired by TIR (Thermal Infrared) sensor mounted on UAV (Unmanned Aerial Vehicle) are used. The acquired images were transformed from JPEG (Joint Photographic Experts Group) to TIFF (Tagged Image File Format) format and orthophoto was then generated. The DN (Digital Number) value of orthophoto was used to calculate the actual land surface temperature. In order to evaluate the accuracy of the calculated land surface temperature, the land surface temperature was compared with the land surface temperature directly observed with an infrared thermometer at the same time. When comparing the observed land surface temperatures in two ways, the accuracy of all the land covers was below the measure accuracy of the TIR sensor. Therefore, the possibility of replacing the satellite image, which is a conventional land surface temperature observation method, is confirmed by using the TIR sensor mounted on UAV.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.63-68
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• 2018

The establishment of Ground Control Points (GCPs) in UAV-Photogrammetry is a working process that requires the most time and expenditure. Recently, the rapid developments of navigation sensors and communication technologies have enabled Unmanned Aerial Vehicles (UAVs) to conduct photogrammetric mapping without using GCP because of the availability of new methods such as RTK (Real Time Kinematic) and PPK (Post Processed Kinematic) technology. In this study, an experiment was conducted to evaluate the potential of RTK-UAV mapping with no GCPs compared to that of non RTK-UAV mapping. The positioning accuracy results produced by images obtained simultaneously from the two different types of UAVs were compared and analyzed. One was a RTK-UAV without GCPs and the other was a non RTK-UAV with different numbers of GCPs. The images were taken with a Canon IXUS 127 camera (focal length 4.3mm, pixel size $1.3{\mu}m$) at a flying height of approximately 160m, corresponding to a nominal GSD of approximately 4.7cm. As a result, the RMSE (planimetric/vertical) of positional accuracy according to the number of GCPs by the non-RTK method was 4.8cm/8.2cm with 5 GCPs, 5.4cm/10.3cm with 4 GCPs, and 6.2cm/12.0cm with 3 GCPs. In the case of non RTK-UAV photogrammetry with no GCP, the positioning accuracy was decreased greatly to approximately 112.9 cm and 204.6 cm in the horizontal and vertical coordinates, respectively. On the other hand, in the case of the RTK method with no ground control point, the errors in the planimetric and vertical position coordinates were reduced remarkably to 13.1cm and 15.7cm, respectively, compared to the non-RTK method. Overall, UAV photogrammetry supported by RTK-GPS technology, enabling precise positioning without a control point, is expected to be useful in the field of spatial information in the future.