• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.317-325
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• 2020

UAV (Unmanned Aerial Vehicle) photogrammetry has recently emerged as a means of obtaining highly precise and rapid spatial information due to its cost-effectiveness and high efficiency. However, current procedures or regulations for quantitative quality verification methods and certification processes for UAV-images are insufficient. In addition, the current verification method for image quality is not evaluated by an MTF (Modulation Transfer Function) analysis or edge response analysis, which can analyze the degree of contrast including image resolution, and only relies on the GSD (Ground Sample Distance) analysis. Therefore, in this study, the edge response analysis using a Slanted edge target was performed along with GSD analysis to confirm the necessity of analyzing edge response analysis in UAV-images quality analysis. Furthermore, a Matlab GUI-based software tool was developed to help streamline the edge response analysis. As a result, we confirmed the need for edge response analysis since the outputs of the edge response analysis from the same GSD had significantly different outcomes. Additionally, we found that the quality of the edge response analysis of UAV-images is proportional to the performance of the camera mounted on the UAV.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.7
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• pp.183-194
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• 2018

With the rapid development of IoT(Internet of Things) technology, various convenient services such as smart home and smart city have been realized. However, IoT devices in unmanned environments are exposed to various security threats including eavesdropping and data forgery, information leakage due to unauthorized access. To build a secure IoT environment, it is necessary to use proper cryptographic technologies to IoT devices. But, it is impossible to apply the technologies applied in the existing IT environment, due to the limited resources of the IoT devices. In this paper, we survey the classification of IoT devices according to the performance and analyze the security requirements for IoT devices. Also we survey and analyze the use of cryptographic technologies in the current status of IoT open standard platform such as AllJoyn, oneM2M, IoTivity. Based on the research of cryptographic usage, we examine whether each platform satisfies security requirements. Each IoT open platform provides cryptographic technology for supporting security services such as confidentiality, integrity, authentication an authorization. However, resource constrained IoT devices such as blood pressure monitoring sensors are difficult to apply existing cryptographic techniques. Thus, it is necessary to study cryptographic technologies for power-limited and resource constrained IoT devices in unattended environments.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.4
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• pp.75-86
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• 2020

The purpose of this study is to develop a technology for estimating rice growth and damage effect according to bacterial leaf blight using UAV multi-spectral imagery. For this purpose, we analyzed the change of aerial images, rice growth factors (plant height, dry weight, LAI) and disease effects according to disease occurrence by using UAV images for 3 rice varieties (Milyang23, Sindongjin-byeo, Saenuri-byeo) from 2017 to 2018. The correlation between vegetation index and rice growth factor during vegetative growth period showed a high value of 0.9 or higher each year. As a result of applying the growth estimation model built in 2017 to 2018, the plant height of Milyang23 showed good error withing 10%. However, it is considered that studies to improve the accuracy of other items are needed. Fixed wing unmanned aerial photographs were also possible to estimate the damage area after 2 to 4 weeks from inoculation. Although sensing data in the multi-spectral (Blue, Green, Red, NIR) band have limitations in early diagnosis of rice disease, for rice varieties such as Milyang23 and Sindongjin-byeo, it was possible to construct the equation of infected leaf area ratio and rice yield estimation using UAV imagery in early and mid-September with high correlation coefficient of 0.8 to 0.9. The results of this study are expected to be useful for farming and policy support related to estimating rice growth, rice plant disease and yield change based on UAV images.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.444-450
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• 2015

The accuracy of small and low cost CCD camera is insufficient to provide data for precisely tracking unmanned aerial vehicles(UAVs). This study shows how UAV can hover on a human targeted tracking object by using CCD camera rather than imprecise GPS data. To realize this, UAVs need to recognize their attitude and position in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for an UAV to estimate of his attitude by environment recognition for UAV hovering, as one of the best important problems. In this paper, we describe a method for the attitude of an UAV using image information of a maker on the floor. This method combines the observed position from GPS sensors and the estimated attitude from the images captured by a fixed camera to estimate an UAV. Using the a priori known path of an UAV in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a marker on the floor and the estimated UAV's attitude. Since the equations are based on the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the UAV. The Kalman filter scheme is applied for this method. its performance is verified by the image processing results and the experiment.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.21-32
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• 2006

The license plate recognition (LPR) unit consists of the following core components: plate region segmentation, individual character extraction, and character recognition. Out of the above three components, accuracy in the performance of plate region segmentation determines the overall recognition rate of the LPR unit. This paper proposes an algorithm for segmenting the license plate region on the front or rear of a vehicle in a fast and accurate manner. In the case of the proposed algorithm images are captured on the spot where unmanned monitoring of illegal parking and stowage is performed with a variety of roadway environments taken into account. As a means of enhancing the segmentation performance of the on-the-spot-captured images of license plate regions, the proposed algorithm uses a mathematical model for license plate colors to convert color images into digital data. In addition, this algorithm uses Gaussian smoothing and double threshold to eliminate image noises, one-pass boundary tracing to do region labeling, and MBR to determine license plate region candidates and extract individual characters from the determined license plate region candidates, thereby segmenting the license plate region on the front or rear of a vehicle through a verification process. This study contributed to addressing the inability of conventional techniques to segment the license plate region on the front or rear of a vehicle where the frame of the license plate is damaged, through processing images in a real-time manner, thereby allowing for the practical application of the proposed algorithm.

• The Korean Journal of Air & Space Law and Policy
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• v.28 no.2
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• pp.37-61
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• 2013

In modern international law, the absence of legal definition regarding drone(Unmanned Aerial Vehicle) has made legal scholars work on an typical analogy between aircraft codified in the international document and drone. The wording of the Convention on International Civil Aviation is limited to two categories of aircraft, such as civil aircraft and state aircraft, whereas military aircraft is not legally defined. As such it is, the current practices of the State regarding the drone flight over foreign territory have proven a hypothese that drone is being deemed as military aircraft. Principal usage of drone lies in reconnaissance and surveillance mission as well as so-called targeted killing, which is prohibited if the killing is treacherous. Claimed war against terrorism, however, is providing a legal rationale that targeted killing is not treacherous, and that the targeted person is not civilian but combatant. In such context, armed attack of drone is deemed legal and justified. Consequently, such attack is legal in the general context of the war. The rules that govern targeting do not turn on the type of weapon system used, and there is no prohibition under the laws of war on the use of technologically advanced weapons systems in armed conflict so long as they are employed in conformity with applicable laws of war. Drones may present interesting new challenges because of their sophistication and the technological advantage they convey to their operators.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.255-257
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• 2017

These days automotive markets are launching models that include a variety of IT technologies. Tesla's Tesla model S and Google's unmanned automobiles are emerging one after another. This type of automobile with IT technology provides various convenience to the driver and the driver is getting benefit by various conveience services. on the contrary, it is also true that defects for errors in electronic components cause accidents that threaten the safety of drivers. There is a sudden unintended acceleration among these accidents. The cause of the accident is not clear yet, but the claim that the ECU device caused by the magnetic field causes accident of the car due is the most reliable. But, in Korea, when occur a car sudden unintended acceleration accident, the char maker often claims that an accident occurred due to driver's pedal malfunction. Also most drivers are responsible for the lack of grounds to refute. In this paper, the pedal operation image of the driver is acquired and the sensor is attached to the control part such as the excel and brake so as to discriminate whether the vehicle sudden unintended acceleration accident is the driver's pedal operation error or the fault of. i have implemented a system that can do this.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.279-282
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• 2017

Kinect depth sensor, a depth camera developed by Microsoft as a natural user interface for game appeared as a very useful tool in computer vision field. In this paper, due to kinect's depth sensor and its high frame rate, we developed a distance measurement system using Kinect camera to test it for unmanned vehicles which need vision systems to perceive the surrounding environment like human do in order to detect objects in their path. Therefore, kinect depth sensor is used to detect objects in its field of view and enhance the distance measurement system from objects to the vision sensor. Detected object is identified in accuracy way to determine if it is a real object or a pixel nose to reduce the processing time by ignoring pixels which are not a part of a real object. Using depth segmentation techniques along with Open CV library for image processing, we can identify present objects within Kinect camera's field of view and measure the distance from them to the sensor. Tests show promising results that this system can be used as well for autonomous vehicles equipped with low-cost range sensor, Kinect camera, for further processing depending on the application type when they reach a certain distance far from detected objects.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.5
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• pp.422-433
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• 2017

Unmanned Aerial Vehicle (UAV) has several advantages over conventional remote sensing techniques. They can acquire high-resolution images quickly and repeatedly. And with a comparatively lower flight altitude, they can obtain good quality images even in cloudy weather. In this paper, we developed for estimating garlic growth at field scale model in major cultivation regions. We used the $NDVI_{UAV}$ that reflects the crop conditions, and seven meteorological elements for 3 major cultivation regions from 2015 to 2017. For this study, UAV imagery was taken at Taean, Changnyeong, and Hapcheon regions nine times from early February to late June during the garlic growing season. Four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.), and fresh weight (F.W.) were measured for twenty plants per plot for each field campaign. The multiple linear regression models were suggested by using backward elimination and stepwise selection in the extraction of independent variables. As a result, model of cold type explain 82.1%, 65.9%, 64.5%, and 61.7% of the P.H., F.W., L.N., P.D. with a root mean square error (RMSE) of 7.98 cm, 5.91 g, 1.05, and 3.43 cm. Especially, model of warm type explain 92.9%, 88.6%, 62.8%, 54.6% of the P.H., P.D., L.N., F.W. with a root mean square error (RMSE) of 16.41 cm, 9.08 cm, 1.12, 19.51 g. The spatial distribution map of garlic growth was in strong agreement with the field measurements in terms of field variation and relative numerical values when $NDVI_{UAV}$ was applied to multiple linear regression models. These results will also be useful for determining the UAV multi-spectral imagery necessary to estimate growth parameters of garlic.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.5
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• pp.391-400
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• 2017

Italian ryegrass (IRG) is one of the fastest growing grasses available to farmers. It offers rapid establishment and starts growing early in the following spring and has fast regrowth after defoliation. So, IRG can be utilized as the dominant/single species of grass used in a farming system, or to play a role as a large producing pasture and sacrificial paddock. The objective of this study was to develop the use of unmanned aerial vehicle (UAV) for the evaluation of feed value of IRG. For this study, UAV imagery was taken on the Nonsan regions two times during the IRG growing season. We analyzed the relationships between $NDVI_{UAV}$ and feed value parameters such as fresh matter yield, dry matter yield, acid detergent fiber (ADF), neutral detergent fiber (NDF), total digestible nutrient (TDN) and crude protein at the season of harvest. Correlation analysis between $NDVI_{UAV}$ and feed value parameters of IRG revealed that $NDVI_{UAV}$ correlated well with crude protein (r = 0.745), and fresh matter yield (r = 0.655). According to the relationship, the variation of $NDVI_{UAV}$ was significant to interpret feed value parameters of IRG. Eight different regression models such as Linear, Logarithmic, Inverse, Quadratic, Cubic, Power, S, and Exponential model were used to estimate IRG feed value parameters. The S and exponential model provided more accurate results to predict fresh matter yield and crude protein than other models based on coefficient of determination, p- and F-value. The spatial distribution map of feed values in IRG plot was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when $NDVI_{UAV}$ was applied to regression equation. These lead to the result that the characteristics of variations in feed value of IRG according to $NDVI_{UAV}$ were well reflected in the model.