• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.83-95
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• 2022

This paper proposes a system that performs object detection and distance estimation for application to autonomous vehicles. Object detection is performed by a network that adjusts the split grid to the input image ratio using the characteristics of the recently actively used deep learning model YOLOv4, and is trained to a custom dataset. The distance to the detected object is estimated using a bounding box and homography. As a result of the experiment, the proposed method improved in overall detection performance and processing speed close to real-time. Compared to the existing YOLOv4, the total mAP of the proposed method increased by 4.03%. The accuracy of object recognition such as pedestrians, vehicles, construction sites, and PE drums, which frequently occur when driving to the city center, has been improved. The processing speed is approximately 55 FPS. The average of the distance estimation error was 5.25m in the X coordinate and 0.97m in the Y coordinate.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.157-167
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• 2023

As a new trend in the development of urban public spaces, the use of right-angle LED screens perfectly combines building facades with naked-eye 3D visual effects, providing designers with a brand-new creative platform. How to create a realistic naked-eye 3D effect on a right-angle LED screen and bring an immersive visual experience to the audience has become a question worth exploring. So far, production companies have yet to announce the relevant design ideas and complete production methods. In order to explore the production principle and production process of the naked-eye 3D effect of the right-angle LED screen, we summarize the basic production principle of the naked-eye 3D impact of the right-angle LED screen through case analysis. Based on understanding the production principle, the actual case production test was carried out, and a complete production process of the naked eye 3D visual effect of the right-angle led screen was tried to be provided by off-line rendering. For the problem of how to deal with image deformation, we provide two production methods: post-production software correction and UV mapping. Among them, the UV mapping method is more efficient and convenient. Referring to this paper can help designers quickly understand the production principle of the naked eye 3D effect of right-angle LED screens. The production process proposed in this paper can provide a reference for production method for related project producers.

• Acta Via Serica
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• v.8 no.1
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• pp.1-30
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• 2023

Traditional Kurdish weavings are among the world's most ancient living textile traditions. One of the largest regional ethnic and linguistic groups, Kurds have inhabited a significant part of Western Asia for millennia. Historically, Kurdish territories were crisscrossed by old and important trade routes, including the Silk Roads. This led to the formation of some of the most significant Kurdish artistic and cultural traditions, including textiles, which influenced and were influenced by those of other non-Kurdish ethnic groups from Caucasia to Central Asia and beyond. One example of Kurdish carpet traditions born in the eighteenth century at the cross-sections of Safavid (1501-1736) urban carpets workshops and centuries-old indigenous Kurdish tribal/rural weaves is senneh gelim or sojaee. A finely flatwoven carpet that was exchanged regionally and internationally as a diplomatic gift and a highly prized commodity. Although in decline, senneh gelims continue to be made by Kurdish women weavers in their original birthplace Sanandaj, the provincial capital of Iranian Kurdistan to date. This study adopts an inter-disciplinary approach to present an image of senneh gelim and women gelim weavers, tracing the developmental trajectories of the craft from the eighteenth century to the present time by drawing on extant art-historical and social scientific studies along with primary ethnographic data collected in Iranian Kurdistan (2018-2019). It investigates the craft tradition's historical origin, various aspects such as techniques, materials, aesthetics, functions, and meanings, and how these transformed over time. Additionally, the paper looks at the social contexts of production, focusing on women carpet weavers and how their socioeconomic and cultural situation has formed senneh carpet production in the past and present and the implications for long-term preservation.

• Fashion & Textile Research Journal
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• v.25 no.2
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• pp.165-174
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• 2023

This study analyzed the purchase status of athleisure wear and consumers' dissatisfaction with online shopping post-COVID-19. The target population comprised female consumers in their 20s to 50s who are interested in exercise and fashion. The study investigated differences according to age. It was found that athleisure wear was purchased once every two to three months and used as sportswear and/or daily wear. Purchase information was obtained via the Internet, and purchases were made online. Design, price, wear sensation, and textiles were the most important selection criteria, and T-shirts and leggings were the most frequently purchased garments. Additionally, textile characteristics such as moisture-absorbing and quick-drying as well as elasticity were evaluated as important. Online shopping of athleisure wear has increased since COVID-19 due to the time savings, low price, opportunity to compare several products, and delivery convenience. However, consumers were dissatisfied due to the differences between the screen image and the actual product, the inconvenience of returns, exchanges, and refunds, the lack of product information, product quality, and delivery. Furthermore, it was found that pursuing value of athleisure wear differed according to age. Consumers in their 20s and 30s required athleisure wear with the characteristics of sportswear and daily or urban wear and those in their 40s and 50s required garments with good performance as sportswear. Based on consumer feedback, it is necessary for manufacturers to provide product information that can improve product reliability.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.12
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• pp.947-954
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• 2017

In case of a fire in urban large structures such as super high-rise buildings, evacuation guidance must be provided to the occupants in order to minimize human deaths and injuries. Therefore, it is essential to provide emergency evacuation guidance when a major fire occurs. In order to effectively support evacuation guidance, it is important to identify major items such as fire location, occupant position, escape route, etc. Also, it is important to quickly identify evacuation areas where residents can safely evacuate from a fire. In this paper, we analyze the CCTV video and propose a method of measuring visibility of the evacuation zone from the smoke caused by the fire in order to determine the safety of evacuation area. To do this, we first extract the background video from the smoke video to measure the visibility of the specific area due to smoke. After generating an edge-extracted image for the extracted background video, the degree of visibility is measured by calculating the change in the edge strength due to smoke.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.535-540
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• 2006

This paper presented an effective strategy to extract the buildings and to reconstruct 3-D buildings using high-resolution multispectral stereo satellite images. Proposed scheme contained three major steps: building enhancement and segmentation using both BDT (Background Discriminant Transformation) and ISODATA algorithm, conjugate building identification using the object matching with Hausdorff distance and color indexing, and 3-D building reconstruction using photogrammetric techniques. IKONOS multispectral stereo images were used to evaluate the scheme. As a result, the BDT technique was verified as an effective tool for enhancing building areas since BDT suppressed the dominance of background to enhance the building as a non-background. In building recognition, color information itself was not enough to identify the conjugate building pairs since most buildings are composed of similar materials such as concrete. When both Hausdorff distance for edge information and color indexing for color information were combined, most segmented buildings in the stereo images were correctly identified. Finally, 3-D building models were successfully generated using the space intersection by the forward RFM (Rational Function Model).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2D
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• pp.311-316
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• 2006

Despite geospatial information systems are widely used in many different fields as a powerful tool for spatial analysis and decision-making, their capabilities to handle realistic 3-D urban environment are very limited. The objective of this work is to integrate the recent developments in 3-D modeling and visualization into GIS to enhance its 3-D capabilities. To achieve a photorealistic view, building models are collected from a pair of aerial stereo images. Roof and wall textures are respectively obtained from ortho-rectified aerial image and ground photography. This study is implemented by using ArcGIS as the work platform and ArcObjects and Visual Basic as development tools. Presented in this paper are 3-D geometric modeling and its data structure, texture creation and its association with the geometric model. As the results, photorealistic views of Purdue University campus are created and rendered with ArcScene.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.45-52
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• 2023

Recently, as the importance of walking in daily life has been emphasized, projects to guarantee walking rights and create a pedestrian environment are being promoted throughout the region. In previous studies, a pedestrian environment assessment was conducted using Jeonju-si road images, and an image comparison pair data set was constructed. However, data sets expressed in numbers have difficulty in generalizing the judgment criteria of pedestrian environment assessors or visually identifying the pedestrian environment preferred by pedestrians. Therefore, this study proposes a method to interpret the results of the pedestrian environment assessment through data visualization by building a web application. According to the semantic segmentation result of analyzing the walking environment components that affect pedestrian environment assessors, it was confirmed that pedestrians did not prefer environments with a lot of "earth" and "grass," and preferred environments with "signboards" and "sidewalks." The proposed study is expected to identify and analyze the results randomly selected by participants in the future pedestrian environment evaluation, and believed that more improved accuracy can be obtained by pre-processing the data purification process.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.776-781
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• 2023

K-UAM will be commercialized through maturity after 2035. Since the Urban Air Mobility (UAM) corridor will be used vertically separating the existing helicopter corridor, the corridor usage is expected to increase. Therefore, a system for monitoring corridors is also needed. In recent years, object detection algorithms have developed significantly. Object detection algorithms are largely divided into one-stage model and two-stage model. In real-time detection, the two-stage model is not suitable for being too slow. One-stage models also had problems with accuracy, but they have improved performance through version upgrades. Among them, YOLO-V5 improved small image object detection performance through Mosaic. Therefore, YOLO-V5 is the most suitable algorithm for systems that require real-time monitoring of wide corridors. Therefore, this paper trains YOLO-V5 and analyzes whether it is ultimately suitable for corridor monitoring.K-uam will be commercialized through maturity after 2035.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.109-123
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• 2024

Research into vision-based end-to-end autonomous driving systems utilizing deep learning and reinforcement learning has been steadily increasing. These systems typically encode continuous and high-dimensional vehicle states, such as location, velocity, orientation, and sensor data, into latent features, which are then decoded into a vehicular control policy. The complexity of urban driving environments necessitates the use of state representation learning through networks like Variational Autoencoders (VAEs) or Convolutional Neural Networks (CNNs). This paper analyzes the impact of different image state encoding methods on reinforcement learning performance in autonomous driving. Experiments were conducted in the CARLA simulator using RGB images and semantically segmented images captured by the vehicle's front camera. These images were encoded using VAE and Vision Transformer (ViT) networks. The study examines how these networks influence the agents' learning outcomes and experimentally demonstrates the role of each state representation technique in enhancing the learning efficiency and decision- making capabilities of autonomous driving systems.