• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.187-196
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• 2020

In this study, collapsed building detection using UAV (Unmanned Aerial Vehicle) and PlanetScope satellite images was carried out, suggesting the possibility of utilization of heterogeneous sensors in object detection located on the surface. To this end, the area where about 20 buildings collapsed due to forest fire damage was selected as study site. First of all, the feature information of objects such as ExG (Excess Green), GLCM (Gray-Level Co-Occurrence Matrix), and DSM (Digital Surface Model) were generated using high-resolution UAV images performed object-based segmentation to detect collapsed buildings. The features were then used to detect candidates for collapsed buildings. In this process, a result of the change detection using PlanetScope were used together to improve detection accuracy. More specifically, the changed pixels acquired by the bitemporal PlanetScope images were used as seed pixels to correct the misdetected and overdetected areas in the candidate group of collapsed buildings. The accuracy of the detection results of collapse buildings using only UAV image and the accuracy of collapse building detection result when UAV and PlanetScope images were used together were analyzed through the manually dizitized reference image. As a result, the results using only UAV image had 0.4867 F1-score, and the results using UAV and PlanetScope images together showed that the value improved to 0.8064 F1-score. Moreover, the Kappa coefficiant value was also dramatically improved from 0.3674 to 0.8225.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.298-303
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• 2010

robots determine the location of the other robot using wireless sensors. Use it to decide how to move his. And go to any location, will make shape of column and line, circle. In this paper, we discuss problem in circle formation enclosing target which moves. It is method about enclosed invader in circle formation based on mutual localization of swarm robot without infrastructure. Therefore, use trilateration that do not need to know the value of the coordinates of reference points. So, Specify enclosed point for the number of robots base on between the relative position of the robot in the coordinate system. And particle filter is proposed to improve the accuracy of the location.

• Journal of the Korean Geotechnical Society
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• v.16 no.3
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• pp.131-144
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• 2000

본 연구는 비검측공 시험법으로서 널리 사용되고 있는 충격반향기법(impact-echo test)의 적용서을 수치해석 및 실내 실험을 통하여 분석해 보았다. 즉, 결함이 없는 말뚝과 결함이 있는 말뚝에 대하여 1차원 및 2차원 축대칭 유한요소해석을 실시하였으며, 또한 모노캐스트라고 하는 일종의 플라스틱 원형 봉의 말뚝에 축대창 공극, 비축대칭 공극, 병목부 및 단면 확대부와 같은 결함을 각각 크기와 깊이를 변화시켜 제작한 후 공기 중과 지반 내부에서 충격반향기법 실험을 수행하였다. 실험결과 충격반향기법의 말뚝 결함 탐지능력은 수치해석에서 얻은 결과와 함께 결함의 크기와 위치에 영향을 받는 것으로 나타났으며 결함의 크기가 커질수록 탐지의 정확도가 향상되는 것을 알 수 있었다. 결함의 상대면적이 말뚝 단면적의 50% 이상이면 충격반향기법에 의하여 결함의 위치를 파악할 수 있는 것으로 나타났으며, 공기 중 보다 지반에 근입 된 말뚝의 경우가 더욱 명확한 신호를 제공해주는 것으로 나타났다. 그리고 시간영역의 신호가 주파수 영역의 신호 보다 말뚝의 결함 크기에 더 민감히 반응하므로 주파수 영역에서 탐지할 수 없는 작은 크기의 결함을 시간 영역에서는 탐지할 수 있는 것으로 나타났다.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.190-196
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• 2023

Recently, following the development of LIDAR technology which can detect distance from the object, the interest for LIDAR based 3D object detection network is getting higher. Previous networks generate inaccurate localization results due to spatial information loss during voxelization and downsampling. In this study, we propose an attention-based convergence method and a camera-LIDAR convergence system to acquire high-level features and high positional accuracy. First, by introducing the attention method into the Voxel-RCNN structure, which is a grid-based 3D object detection network, the multi-scale sparse 3D convolution feature is effectively fused to improve the performance of 3D object detection. Additionally, we propose the late-fusion mechanism for fusing outcomes in 3D object detection network and 2D object detection network to delete false positive. Comparative experiments with existing algorithms are performed using the KITTI data set, which is widely used in the field of autonomous driving. The proposed method showed performance improvement in both 2D object detection on BEV and 3D object detection. In particular, the precision was improved by about 0.54% for the car moderate class compared to Voxel-RCNN.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.737-746
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• 2022

Utilizing weather independent SAR images along with machine learning based object detector is effective in robust vessel monitoring. While conventional SAR images often applied amplitude data from Single Look Complex, exploitation of polarimetric parameters acquired from multiple polarimetric SAR images was yet to be implemented to vessel detection utilizing machine learning. Hence, this study used four polarimetric parameters (H, p₁, DoP, DPRVI) retrieved from eigen-decomposition and two backscattering coefficients (γ₀, _VV, γ₀, _VH) from radiometric calibration; six bands in total were respectively exploited from 52 Sentinel-1 SAR images, accompanied by vessel training data extracted from AIS information which corresponds to acquisition time span of the SAR image. Evaluating different cases of combination, the use of polarimetric indexes along with amplitude values derived enhanced vessel detection performances than that of utilizing amplitude values exclusively.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.630-636
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• 2019

Passive Ranging Sonar (PRS) is a type of passive sonar consisting of three sub-array on the port and starboard, and has a characteristic of detecting a target and calculating a bearing and a distance. The bearing and distance calculation requires physical sub-array position information, and the bearing and distance accuracy performance are deteriorated when the position information of the sub-array is inaccurate. In particular, it has a greater impact on distance accuracy performance using plus value of two time-delay than a bearing using average value of two time-delay. In order to improve this, a study on sub-array position error estimation and error compensation is needed. In this paper, We estimate the sub-array position error based on enetic algorithm, an optimization search technique, and propose a method to improve the performance of distance accuracy by compensating the time delay error caused by the position error. In addition, we will verify the proposed algorithm and its performance using the sea-going data.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.109-119
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• 2013

An unmanned ground vehicle(UGV) today plays a significant role in both civilian and military areas. Predominantly these systems are used to replace humans in hazardous situations. To take unmanned ground vehicles systems to the next level and increase their capabilities and the range of missions they are able to perform in the combat field, new technologies are needed in the area of command and control. For this reason, we present war field simulator based on information fusion technology to efficiently control UGV. In this paper, we present the war field simulator which is made of critical components, that is, simulation controller, virtual image viewer, and remote control device to efficiently control UGV in the future combat fields. In our information fusion technology, improved methods of target detection, recognition, and location are proposed. In addition, time reduction method of target detection is also proposed. In the consequence of the operation test, we expect that our war field simulator based on information fusion technology plays an important role in the future military operation significantly.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.213-216
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• 2009

유한요소모델개선기법은 계측된 동특성을 모사하는 구조해석모델을 구하는 방법으로서 손상탐지 및 구조건전도감시를 위해 효과적으로 이용될 수 있다. 유한요소모델개선기법에는 다양한 종류의 동특성데이터가 사용될 수 있는데, 본 연구에서는 고유진동수와 모드형상을 사용한 경우와 고유진동수와 주파수응답함수를 사용한 경우를 각각 사용해 실험실 규모의 구조물의 손상 위치 및 손상정도를 추정하였다. 4층 철골조의 골조구조물로서 진동대를 이용하여 원구조물에 백색잡음 가진실험을 실시한 후 손상의 모사를 위해 1층 부분의 보 부재를 작은 단면의 부재로 교체하고 동일한 실험을 반복하였다. 보 부재 교체 전 후에 계측된 데이터와 두 종류의 모델개선기법을 각각 적용하여 손상탐지를 실시한 후 그 정확도를 분석하였다.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.279-282
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• 2006

분광혼합은 위성영상에서 공간해상도의 한계로 인해 다른 분광 속성을 가진 물질들이 하나의 픽셀 내에 존재하게 될 때 발생하게 된다. 이러한 문제를 해결하고자 분광분리 알고리즘을 통해 픽셀의 순수한 영역만을 선정하여 정확도 높은 탐지가 가능하도록 하는 분광혼합분석(Spectral Mixture Analysis, 이하 SMA)을 고해상도 영상에 적용하였다. 본 연구는 산림의 훼손이 심각한 강원도 정선군 임계지역의 QuickBird 다중분광 위성영상을 이용하였다. 주성분분석(Principal Component Analysis, 이하 PCA)으로 생성된 결과 영상의 1, 2, 3번 밴드를 추출한 후에 밴드간의 Scatter plots 내에서 끝지점에 위치하는 Endmember를 3개(나지, 산림, 초지) 선정하였다. 선정된 Endmember를 토대로 작성된 fraction 영상을 이용하여 강원도 임계지역의 산림훼손으로 초지와 나지로 변화된 지역을 탐지하여 보았다.