• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.12
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• pp.1115-1123
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• 2011

This paper proposes an image processing algorithms for detection and tracking of aerial vehicles in short-range. Proposed algorithm detects moving objects by using image homography calculated from consecutive video frames and determines whether the detected objects are approaching aerial vehicles by the Probabilistic Multi-Hypothesis Tracking method(PMHT). This algorithm can perform better than simple color-based detection methods since it can detect moving objects under complex background such as the ground seen during low altitude flight and consider the characteristics of vehicle dynamics. Furthermore, it is effective for the flight test due to the reduction of thresholding sensitivity against external factors. The performance of proposed algorithm is verified by applying to the onboard video obtained by flight test.

• Journal of the Korean Institute of Rural Architecture
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• v.24 no.1
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• pp.9-17
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• 2022

Rural spaces are increasingly valuable as areas for introducing renewable energy infrastructure to achieve carbon neutrality. Rural areas are the living grounds of rural residents, and the balance of conservation and development for rural areas is important for the introduction of reasonable facilities. In order to maintain a balance between development and preservation and to introduce reasonable renewable energy facilities, it is necessary to develop a current status survey and an effective survey method to utilize a space capable of introducing renewable energy facilities such as idle land and vacant houses. Therefore, this study was conducted to verify the readability using an unmanned aerial vehicle, and the main results are as follows. The detection of photovoltaic power generation facilities using unmanned aerial vehicles was effective in analyzing the location and area of photovoltaic panels located on the roofs of buildings, and it was possible to calculate the expected power generation by region through the area calculation of photovoltaic panels. The vacant house detection can be used to select an investigation target for an vacant house condition survey as it can identify damage to buildings that are expected to be empty houses, management status, and electricity supply facilities through aerial photos. It is judged that the unmanned aerial vehicle detection capability can be utilized as a method to improve the efficiency of investigation and supplement the data related to solar power generation facilities and vacant houses provided by public institutions. Although this study detected the status of solar power generation facilities and vacant houses through high-resolution aerial image analysis, as a follow-up study, automatic measurement methods using the temperature difference of solar power generation facilities and general characteristics of vacant houses that can be read from the air were investigated. If the deriving research is carried out, it is judged that it will be possible to contribute to the improvement of the accuracy of the detection result using the unmanned aerial vehicle and the expansion of the application range.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.5
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• pp.708-716
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• 2017

We deal with the target selection in seeker-detection image through network, using the detection information from aerial vehicle and the target information from surveillance and reconnaissance system. Especially, we constrain the sea battle environment, where it is difficult to perform scene-matching rather than land. In this paper, we suggest the target selection algorithm based on the confidence estimation with respect to distance and size. In detail, we propose the generation method of reference point for distance evaluation, and we investigate the effect of pixel margin and target course for size evaluation. Finally, the proposed algorithm is simulated and analyzed through several scenarios.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.32-39
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• 2023

This paper provides a comprehensive overview of UAV classification, tracking, and detection, offering researchers a clear understanding of these fundamental concepts. It elucidates how classification categorizes UAVs based on attributes, how tracking monitors real-time positions, and how detection identifies UAV presence. The interconnectedness of these aspects is highlighted, with detection enhancing tracking and classification aiding in anomaly identification. Moreover, the paper emphasizes the relevance of simulations in the context of drones and UAVs, underscoring their pivotal role in training, testing, and research. By succinctly presenting these core concepts and their practical implications, the paper equips researchers with a solid foundation to comprehend and explore the complexities of UAV operations and the role of simulations in advancing this dynamic field.

• Korean Journal of Computational Design and Engineering
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• v.18 no.3
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• pp.155-166
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• 2013

We present recent human-vehicle interaction (HVI) research conducted in the area of defense and military application. Research topics discussed in this paper include: training simulation for overland navigation tasks; expertise effects in overland navigation performance and scan patterns; pilot's perception and confidence on an overland navigation task; effects of UAV (Unmanned Aerial Vehicle) supervisory control on F-18 formation flight performance in a simulator environment; autonomy balancing in a manned-unmanned teaming (MUT) swarm attack, enabling visual detection of IED (Improvised Explosive Device) indicators through Perceptual Learning Assessment and Training; usability test on DaViTo (Data Visualization Tool); and modeling peripheral vision for moving target search and detection. Diverse and leading HVI study in the defense domain suggests future research direction in other HVI emerging areas such as automotive industry and aviation domain.

• 한국항공운항학회:학술대회논문집
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• 2005.11a
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• pp.124-127
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• 2005

• ETRI Journal
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• v.45 no.5
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• pp.795-810
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• 2023

Unmanned aerial vehicles (UAV) and ground vehicles (UGV) require advanced video analytics for various tasks, such as moving object detection and segmentation; this has led to increasing demands for these methods. We propose a zero-shot video object segmentation method specifically designed for UAV and UGV applications that focuses on the discovery of moving objects in challenging scenarios. This method employs a background memory model that enables training from sparse annotations along the time axis, utilizing temporal modeling of the background to detect moving objects effectively. The proposed method addresses the limitations of the existing state-of-the-art methods for detecting salient objects within images, regardless of their movements. In particular, our method achieved mean J and F values of 82.7 and 81.2 on the DAVIS'16, respectively. We also conducted extensive ablation studies that highlighted the contributions of various input compositions and combinations of datasets used for training. In future developments, we will integrate the proposed method with additional systems, such as tracking and obstacle avoidance functionalities.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.4
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• pp.511-523
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• 2023

Research on the integration of unmanned aerial vehicles and deep learning for reinforced concrete damage detection is actively underway. Convolutional neural networks have a high impact on the performance of image classification, detection, and segmentation as backbones. The MobileNet, a pre-trained convolutional neural network, is efficient as a backbone for an unmanned aerial vehicle-based damage detection model because it can achieve sufficient accuracy with low computational complexity. Analyzing vanilla convolutional neural networks and MobileNet under various conditions, MobileNet was evaluated to have a verification accuracy 6.0~9.0% higher than vanilla convolutional neural networks with 15.9~22.9% lower computational complexity. MobileNetV2, MobileNetV3Large and MobileNetV3Small showed almost identical maximum verification accuracy, and the optimal conditions for MobileNet's reinforced concrete damage image feature extraction were analyzed to be the optimizer RMSprop, no dropout, and average pooling. The maximum validation accuracy of 75.49% for 7 types of damage detection based on MobilenetV2 derived in this study can be improved by image accumulation and continuous learning.

• Journal of Forest and Environmental Science
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• v.35 no.3
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• pp.181-188
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• 2019

This study was performed to construct tree species classification map according to three information types (spectral information, texture information, and spectral and texture information) by altitude (30 m, 60 m, 90 m) using the unmanned aerial vehicle images and the object-based classification method, and to evaluate the concordance rate through field survey data. The object-based, optimal weighted values by altitude were 176 for 30 m images, 111 for 60 m images, and 108 for 90 m images in the case of Scale while 0.4/0.6, 0.5/0.5, in the case of the shape/color and compactness/smoothness respectively regardless of the altitude. The overall accuracy according to the type of information by altitude, the information on spectral and texture information was about 88% in the case of 30 m and the spectral information was about 98% and about 86% in the case of 60 m and 90 m respectively showing the highest rates. The concordance rate with the field survey data per tree species was the highest with about 92% in the case of Pinus densiflora at 30 m, about 100% in the case of Prunus sargentii Rehder tree at 60 m, and about 89% in the case of Robinia pseudoacacia L. at 90 m.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1392-1413
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• 2022

In order to meet the rapid development of the unmanned aerial vehicle (UAV) communication needs, cooperative spectrum sensing (CSS) helps to identify unused spectrum for the primary users (PU). However, multi-UAV mode (MUM) requires the large communication resource in a cognitive UAV network, resulting in a severe decline of spectrum efficiency (SE) and energy efficiency (EE) and increase of energy consumption (EC). On this account, we extend the traditional 2D spectrum space to 3D spectrum space for the UAV network scenario and enable UAVs to proceed with spectrum sensing behaviors in this paper, and propose a novel multi-slot mode (MSM), in which the sensing slot is divided into multiple mini-slots within a UAV. Then, the CSS process is developed into a composite hypothesis testing problem. Furthermore, to improve SE and EE and reduce EC, we use the sequential detection to make a global decision about the PU channel status. Based on this, we also consider a truncation scenario of the sequential detection under the sensing delay constraint, and further derive a closed-form performance expression, in terms of the CSS performance and cooperative efficiency. At last, the simulation results verify that the performance and cooperative efficiency of MSM outperforms that of the traditional MUM in a low EC.