• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.3
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• pp.317-325
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• 2021

Pine wilt disease first appeared in Busan in 1998; it is a serious disease that causes enormous damage to pine trees. The Korean government enacted a special law on the control of pine wilt disease in 2005, which controls and prohibits the movement of pine trees in affected areas. However, existing forecasting and control methods have physical and economic challenges in reducing pine wilt disease that occurs simultaneously and radically in mountainous terrain. In this study, the authors present the use of a deep learning object recognition and prediction method based on visual materials using an unmanned aerial vehicle (UAV) to effectively detect trees suspected of being infected with pine wilt disease. In order to observe pine wilt disease, an orthomosaic was produced using image data acquired through aerial shots. As a result, 198 damaged trees were identified, while 84 damaged trees were identified in field surveys that excluded areas with inaccessible steep slopes and cliffs. Analysis using image segmentation (SegNet) and image detection (YOLOv2) obtained a performance value of 0.57 and 0.77, respectively.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.2
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• pp.43-48
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• 2021

Precast concrete can be used to reduce construction period and enhance construct ability. However structural problems could be occurred due to the wrong application of boundary condition and misunderstanding of structural behavior in the process of segmentation of original structure system. I experienced a serious deflections and cracks due to the increase of bending moment and creep after the construction of precast concrete slab, and we learned that this is from the misunderstanding of support conditions and structure behaviors of precast slab panel. Two support columns under the precast slab are inserted to reduce the bending moment, and the camber according to jacking force should be estimated for the structural safety during the reinforcing work. A proper support condition and the flexural stiffness of precast concrete slab were applied to check the deflection and crack for existing structure by inverse analysis, and we can estimate the camber according to jacking force of the precast concrete slab, and suggest a method to make safe structure.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.1-8
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• 2021

Ultra high-quality and ultra high-resolution omnidirectional 360-degree video streaming is needed to provide immersive media through head-mounted display(HMD) in virtual reality environment, which requires high bandwidth and computational complexity. One of the approaches avoiding these problems is to apply viewport-dependent selective streaming using tile-based segmentation method. This paper presents a performance analysis of viewport-dependent tiled streaming on 16K ultra high-quality 360-degree videos and 4K 360-degree videos which are widely used. Experimental results showed 42.47% of bjotegaard delta rate(BD-rate) saving on 16K ultra high-quality 360-degree video tiled streaming compared to viewport-independent streaming while 4K 360-degree video showed 26.41% of BD-rate saving. Therefore, this paper verified that tiled streaming is more efficient on ultra-high quality video.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.95-105
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• 2021

Road surface damage detection is essential for a comfortable driving environment and the prevention of safety accidents. Road management institutes are using automated technology-based inspection equipment and systems. As one of these automation technologies, a sensor to detect road surface damage plays an important role. For this purpose, several studies on sensors using deep learning have been conducted in recent years. Road images and label images are needed to develop such deep learning algorithms. On the other hand, considerable time and labor will be needed to secure label images. In this paper, the adversarial learning method, one of the semi-supervised learning techniques, was proposed to solve this problem. For its implementation, a lightweight deep neural network model was trained using 5,327 road images and 1,327 label images. After experimenting with 400 road images, a model with a mean intersection over a union of 80.54% and an F1 score of 77.85% was developed. Through this, a technology that can improve recognition performance by adding only road images was developed to learning without label images and is expected to be used as a technology for road surface management in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.389-395
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• 2022

Because of improvement of deep learning techniques, deep learning using computer vision such as classification, detection and segmentation has also been used widely at many fields. Expecially, automatic driving is one of the major fields that applies computer vision systems. Also there are a lot of works and researches to combine multiple tasks in a single network. In this study, we propose the network that predicts the individual depth of pedestrians and vehicles. Proposed model is constructed based on YOLOv3 for object detection and Monodepth for depth estimation, and it process object detection and depth estimation consequently using encoder and decoder based on hard parameter sharing. We also used attention module to improve the accuracy of both object detection and depth estimation. Depth is predicted with monocular image, and is trained using self-supervised training method.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1589-1605
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• 2022

Phase unwrapping is an essential procedure for interferometric synthetic aperture radar techniques. Accordingly, a lot of phase unwrapping methods have been developed. Deep-learning-based unwrapping methods have recently been proposed. In this paper, we reviewed state-of-the-art deep-learning-based unwrapping approaches in terms of 1) the approaches to predicting unwrapped phases, 2) deep learning model structures for phase unwrapping, and 3) training data generation. The research trend of the approaches to predicting unwrapped phases was introduced by categorizing wrap count segmentation, phase jump classification, phase regression, and deep-learning-assisted method. We introduced the case studies of deep learning model structure for phase unwrapping, and model structure optimization to relate the overall phase information. In addition, we summarized the research trend of the training data generation approaches in the views of phase gradient and noise in the main. And the future direction in deep-learning-based phase unwrapping was presented. It is expected that this paper is used as guideline for exploring future direction of deep-learning-based phase unwrapping research in Korea.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.1-10
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• 2022

Attempts to interpret human perspectives using computer vision have been developed in various fields. In this paper, we propose a method for evaluating the walking environment through semantic segmentation results of images from road images. First, the Kakao Map API was used to collect road images, and four-way images were collected from about 50,000 points in JeonJu. 20% of the collected images build datasets through crowdsourcing-based paired comparisons, and train various regression models using paired comparison data. In order to derive the walkability score of the image data, the ranking score is calculated using the Trueskill algorithm, which is a ranking algorithm, and the walkability and analysis using various regression models are performed using the constructed data. Through this study, it is shown that the walkability of Jeonju can be evaluated and scores can be derived through the correlation between pixel distribution classification information rather than human vision.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.638-653
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• 2022

In this paper, we propose the new method of creating 3D virtual-human reflecting the pattern of clothes worn by the person in the high-resolution whole body front image and the body shape data about the person. To get the pattern of clothes, we proceed Instance Segmentation and clothes parsing using Cascade Mask R-CNN. After, we use Pix2Pix to blur the boundaries and estimate the background color and can get UV-Map of 3D clothes mesh proceeding UV-Map base warping. Also, we get the body shape data using SMPL-X and deform the original clothes and body mesh. With UV-Map of clothes and deformed clothes and body mesh, user finally can see the animation of 3D virtual-human reflecting user's appearance by rendering with the state-of-the game engine, i.e. Unreal Engine.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.5
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• pp.259-266
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• 2022

In this study, we developed a deep learning and vision sensor-based assembly performance evaluation method isfor prefabricated steel structures. The assembly parts were segmented using a modified version of the receptive field block convolution module inspired by the eccentric function of the human visual system. The quality of the assembly was evaluated by detecting the bolt holes in the segmented assembly part and calculating the bolt hole positions. To validate the performance of the evaluation, models of standard and defective assembly parts were produced using a 3D printer. The assembly part segmentation network was trained based on the 3D model images captured from a vision sensor. The sbolt hole positions in the segmented assembly image were calculated using image processing techniques, and the assembly performance evaluation using the k-nearest neighbor algorithm was verified. The experimental results show that the assembly parts were segmented with high precision, and the assembly performance based on the positions of the bolt holes in the detected assembly part was evaluated with a classification error of less than 5%.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.83-91
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• 2020

The purpose of this study is to develop creativity-based creative convergence subjects for nursing students. For the purpose of this study, the procedures are conducted that the needs analysis, setting educational goals, segmentation of educational goals, selection of educational contents and organization by F. Bobbitt's curriculum development model and the creative convergence subject was developed through the verification process of the validity of experts. Through a theoretical review, the contents of education in creative convergence subjects consisted of converging with other areas, focusing on creativity. It was presented as a liberal arts subject with two credits, and as an educational method, an online class utilizing blended learning and offline classes centered on activities by teams were presented. In addition, the curriculum was divided into understanding, application, synthesis, and deepening so that students could understand the concept of creative convergence thinking and apply it through thinking techniques and strategies, and finally improve their creative convergence thinking abilities through team projects.