• 대한토목학회논문집
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• 제32권2D호
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• pp.129-136
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• 2012

무인항공기(UAV: Unmanned Aerial Vehicle), 헬리콥터, 항공기를 이용하여 실시간 교통자료를 수집하는 항공 기반 차량 검지시스템(ADS: Aerial-Based Vehicle Detection System)에 관한 연구가 미국, 일본, 독일에서 이루어져 왔다. 따라서 본 연구에서는 ADS의 교통자료 수집 시스템으로 활용 가능성을 검토하기 위하여 먼저 ADS에 의하여 수집된 자료가 이미지프로세싱 등 자료추출 기법을 거쳐 통행속도 등 교통정보를 산출할 수 있는 지를 확인하였다. 다음으로는 ADS에 의하여 수집된 자료의 신뢰성 정도가 교통정보 제공에 적합한 지를 확인하였다. 그 결과 ADS는 기존에 상시적으로 실시간 교통정보 제공을 하기 위하여 사용되고 있는 VDS 등을 대체하기에는 기술적 비용적 측면에서 어려움이 있을 것으로 파악되었다. 하지만 재해 발생 등 비반복적 교통상황이 장시간 발생할 경우 비상교통관리대책 등을 세우기 위한 보완적 방안으로 활용할 수 있을 것이다.

• 대한원격탐사학회지
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• 제37권4호
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• pp.747-761
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• 2021

With the increasing application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become an essential engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial images based on the YOLOv4 deep learning algorithm is presented. At present, the most known datasets are VOC (The PASCAL Visual Object Classes Challenge), ImageNet, and COCO (Microsoft Common Objects in Context), which comply with the vehicle detection from UAV. An integrated dataset not only reflects its quantity and photo quality but also its diversity which affects the detection accuracy. The method integrates three public aerial image datasets VAID, UAVD, DOTA suitable for YOLOv4. The training model presents good test results especially for small objects, rotating objects, as well as compact and dense objects, and meets the real-time detection requirements. For future work, we will integrate one more aerial image dataset acquired by our lab to increase the number and diversity of training samples, at the same time, while meeting the real-time requirements.

• 대한원격탐사학회지
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• 제24권5호
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• pp.473-481
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• 2008

This paper presents an approach to Back-propagation and Radial Basis Function neural network method with various training set for automatic vehicle detection from aerial images. The initial extraction of candidate object is based on Mean-shift algorithm with symmetric property of a vehicle structure. By fusing the density and the symmetry, the method can remove the ambiguous objects and reduce the cost of processing in the next stage. To extract features from the detected object, we describe the object as a log-polar shape histogram using edge strengths of object and represent the orientation and distance from its center. The spatial histogram is used for calculating the momentum of object and compensating the direction of object. BPNN and RBFNN are applied to verify the object as a vehicle using a variety of non-car training sets. The proposed algorithm shows the results which are according to the training data. By comparing the training sets, advantages and disadvantages of them have been discussed.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.260-262
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• 2006

One of future remote sensing techniques for transportation application is vehicle detection from the space, which could be the basis of measuring traffic volume and recognizing traffic condition in the future. This paper introduces an approach to vehicle detection using image object segmentation approach. The object-oriented image processing is particularly beneficial to high-resolution image classification of urban area, which suffers from noisy components in general. The project site was Dae-Jeon metropolitan area and a set of true color aerial images at 10cm resolution was used for the test. Authors investigated a variety of parameters such as scale, color, and shape and produced a customized solution for vehicle detection, which is based on a knowledge-based hierarchical model in the environment of eCognition. The highest tumbling block of the vehicle detection in the given data sets was to discriminate vehicles in dark color from new black asphalt pavement. Except for the cases, the overall accuracy was over 90%.

• 정보보호학회논문지
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• 제32권1호
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• pp.39-48
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• 2022

본 논문에서는 무인 비행체 내부 네트워크의 이상 징후를 탐지하는 시퀀스 기반 이상 탐지 시스템을 제안한다. 제안하는 이상 탐지 시스템은 무인 비행체가 지상 통제 시스템에 주기적으로 전송하는 상태 메시지 시퀀스들 간의 유사도를 측정하여 이상 징후를 탐지한다. 본 연구에서는 무인 비행체 내부 네트워크에서 수행 가능한 악의적인 메시지 주입 공격 세 가지를 정의하고, 해당 공격 기법들을 Pixhawk4 쿼드콥터에서 시뮬레이션하였다. 결과적으로, 제안하는 이상 탐지 시스템은 96% 이상의 정확도로 비정상 시퀀스를 탐지할 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1989-2011
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• 2019

Vehicle detection based on aerial images is an interesting and challenging research topic. Most of the traditional vehicle detection methods are based on the sliding window search algorithm, but these methods are not sufficient for the extraction of object features, and accompanied with heavy computational costs. Recent studies have shown that convolutional neural network algorithm has made a significant progress in computer vision, especially Faster R-CNN. However, this algorithm mainly detects objects in natural scenes, it is not suitable for detecting small object in aerial view. In this paper, an accurate and effective vehicle detection algorithm based on Faster R-CNN is proposed. Our method fuse a hyperactive feature map network with Eltwise model and Concat model, which is more conducive to the extraction of small object features. Moreover, setting suitable anchor boxes based on the size of the object is used in our model, which also effectively improves the performance of the detection. We evaluate the detection performance of our method on the Munich dataset and our collected dataset, with improvements in accuracy and effectivity compared with other methods. Our model achieves 82.2% in recall rate and 90.2% accuracy rate on Munich dataset, which has increased by 2.5 and 1.3 percentage points respectively over the state-of-the-art methods.

• 대한건축학회논문집:구조계
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• 제35권1호
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• pp.11-19
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• 2019

Recently, with the development of the fourth industrial revolution that has been achieved through the fusion of information and communication technology (ICT), the technologies of AI, IOT, BIG-DATA, it is increasing utilization rate by industry and research and development of application technologies are being actively carried out. Especially, in the case of unmanned aerial vehicles, the construction market is expected to be one of the most commercialized areas in the world for the next decade. However, research on utilization of unmanned aerial vehicles in the construction field in Korea is insufficient. In this study, We have developed a quantitative building inspection method using the unmanned aerial vehicle and presented the protocol for it. The proposed protocol was verified by applying it to existing old buildings, and defect information could be quantified by calculating length, width, and area for each defect. Through this technical research, the final goal is to contribute to the development of safety diagnosis technology using unmanned aerial vehicle and risk assessment technology of buildings in case of disaster such as earthquake.

• 한국재난정보학회 논문집
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• 제17권3호
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• pp.556-567
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• 2021

연구목적: 본 연구에서는 무인이동체를 활용한 철도교량의 외관조사 점검을 보다 효율적이고 신뢰성 있게 점검을 위하여 무인이동체를 통해 촬영된 이미지를 바탕으로 다양한 방식의 딥러닝 기반 자동 손상 분석기술을 검토하였다. 연구방법: 취득된 이미지를 바탕으로 손상항목을 정의하고 학습데이터로 추출하여 딥러닝 분석 모델을 생성하였다. 그리고 철도교량의 외관 손상 중 균열, 콘크리트 박리·박락, 누수, 철근노출에 대한 손상 이미지를 학습한 모델을 적용하여 자동 손상 분석 결과로 테스트하였다. 연구결과: 분석 결과 평균 95%이상 검측 재현율을 도출하는 분석 기법을 검토할 수 있었다. 이와 같은 분석 기술은 기존 육안점검 결과 대비 보다 객관적이고 정밀한 손상 검측이 가능하다. 결론: 본 연구를 통해 개발된 기술을 통해 철도 유지관리 분야에서 무인이동체를 활용한 정기점검 시 자동손상분석을 통한 객관적인 결과도출과 기존 대비 소요시간, 비용저감이 가능할 것으로 기대된다.

• 한국측량학회지
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• 제39권6호
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• pp.555-562
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• 2021

최근 공간정보 구축 분야에서 무인항공기는 신속한 데이터 취득과 활용이 가능하여 활용이 증대되고 있으며, 고정익, 회전익, 수직이착륙형 등 다양한 형태의 무인항공기가 출시되고 있다. 본 연구에서는 고정익, 회전익, 수직이착륙형 무인항공기를 이용하여 사진측량을 수행하였으며, 2가지 종류의 무인항공 LiDAR (Light Detection And Ranging) 센서를 이용하여 공간정보를 구축하였다. 또한, 무인항공 사진측량 및 LiDAR를 통해 구축된 공간정보의 활용성을 제시하기 위해 정확도를 평가하였다. 정확도 평가 결과 무인항공 사진측량을 통해 구축된 정사영상은 수평방향 2cm 이내의 정확도를 나타내었다. 구축된 정사영상의 GSD (Ground Sample Distance)가 2cm 정도임을 볼 때 무인항공 사진측량 성과물의 수평방향 정확도는 GSD 이내로 판단된다. 무인항공 LiDAR를 통해 구축된 공간정보는 높이방향으로 평균 6cm 이내의 정확도를 나타내었으며 식생 지역에서 지면에 대한 데이터 취득이 가능하였다. LiDAR 데이터를 활용한 DEM (Digital Elevation Model)은 건설시공, 도시계획, 재난 예방, 지형분석 등 다양한 활용이 가능할 것이다.

• 로봇학회논문지
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• 제9권3호
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• pp.154-159
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• 2014

The Global Positioning System (GPS) is widely used to aid the navigation of aerial vehicles. However, the GPS cannot be used indoors, so alternative navigation methods are needed to be developed for micro aerial vehicles (MAVs) flying in GPS-denied environments. In this paper, a real-time three-dimensional (3-D) indoor navigation system and closed-loop control of a quad-rotor aerial vehicle equipped with an inertial measurement unit (IMU) and a low-cost light detection and ranging (LIDAR) is presented. In order to estimate the pose of the vehicle equipped with the two-dimensional LIDAR, an octree-based grid map and Monte-Carlo Localization (MCL) are adopted. The navigation results using the MCL are then evaluated by making a comparison with a motion capture system. Finally, the results are used for closed-loop control in order to validate its positioning accuracy during procedures for stable hovering and waypoint-following.