• Journal of IKEEE
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• v.25 no.1
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• pp.83-87
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• 2021

In harsh environments such as fog or fine dust, the cameras' detection ability for object recognition may significantly decrease. In order to accurately obtain important information even in bad weather, fog removal algorithms are necessarily required. Research has been conducted in various ways, such as computer vision/data-based fog removal technology. In those techniques, estimating the amount of fog through the input image's depth information is an important procedure. In this paper, a linear model is presented under the assumption that the image dark channel dictionary, saturation ∗ value, and sharpness characteristics are linearly related to depth information. The proposed method of haze removal through a linear model shows the superiority of algorithm performance in quantitative numerical evaluation.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.2
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• pp.323-328
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• 2021

Diagnostic models are required. Data augmentation is one of the best ways to improve deep learning performance. Traditional augmentation techniques that modify image brightness or spatial information are difficult to achieve great results. To overcome this, a generative adversarial network (GAN) technology that generates virtual data to increase deep learning performance has emerged. GAN can create realistic-looking fake images by competitive learning two networks, a generator that creates fakes and a discriminator that determines whether images are real or fake made by the generator. GAN is being used in computer vision, IT solutions, and medical imaging fields. It is essential to secure additional learning data to advance deep learning-based fault diagnosis solutions in the power industry where facilities are strictly maintained more than other industries. In this paper, we propose a method for generating power facility images using GAN and a strategy for improving performance when only used a small amount of data. Finally, we analyze the performance of the augmented image to see if it could be utilized for the deep learning-based diagnosis system or not.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.2_1
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• pp.217-223
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• 2023

With the rapid advancement of technologies, need for different research fields where this technology can be used is also increasing. One of the most researched topic in computer vision is object detection, which has widely been implemented in various fields which include healthcare, video surveillance and education. The main goal of object detection is to identify and categorize all the objects in a target environment. Specifically, methods of object detection consist of a variety of significant techniq ues, such as image processing and patterns recognition. Anomaly detection is a part of object detection, anomalies can be found various scenarios for example crowded places such as subway stations. An abnormal event can be assumed as a variation from the conventional scene. Since the abnormal event does not occur frequently, the distribution of normal and abnormal events is thoroughly imbalanced. In terms of public safety, abnormal events should be avoided and therefore immediate action need to be taken. When abnormal events occur in certain places, real time detection is required to prevent and protect the safety of the people. To solve the above problems, we propose a modified YOLOv5 object detection algorithm by implementing dilated convolutional layers which achieved 97% mAP50 compared to other five different models of YOLOv5. In addition to this, we also created a simple mobile application to avail the abnormal event detection on mobile phones.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1597-1610
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• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.376-378
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• 2022

Artificial intelligence technology is widely used in various fields such as artificial intelligence speakers, artificial intelligence chatbots, and autonomous vehicles. Among these AI application fields, the image processing field shows various uses such as detecting objects or recognizing objects using artificial intelligence. In this paper, data synthesized by a virtual human is used as a method to analyze images taken in a specific space.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1671-1678
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• 2023

The Korea multi-purpose satellite (KOMPSAT) series consisting of multi-sensors has been used in various fields such as land, environmental monitoring, and disaster analysis since its first launch in 1999. Recently, as various information processing technologies (high-speed computing technology, computer vision, artificial intelligence, etc.) that are rapidly developing are utilized in the field of remote sensing, it has become possible to develop more various satellite image processing and analysis algorithms. In this special issue, we would like to introduce recently researched technologies related to the KOMPSAT image application and research topics participated in the 2023 Satellite Information Application Contest.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.1-8
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• 2024

Robotic arms have been widely utilized in various labor-intensive industries such as manufacturing, agriculture, and food services, contributing to increasing productivity. In the development of industrial robotic arms, camera sensors have many advantages due to their cost-effectiveness and small sizes. However, estimating object positions is a challenging problem, and it critically affects to the robustness of object manipulation functions. This paper proposes a method for estimating the 3D positions of objects, and it is applied to a pick-and-place task. A deep learning model is utilized to detect 2D bounding boxes in the image plane, and the pinhole camera model is employed to compute the object positions. To improve the robustness of measuring the 3D positions of objects, we analyze the effect of lens distortion and introduce a false positive filtering process. Experiments were conducted on a real-world scenario for moving medicine bottles by using a camera-based manipulator. Experimental results demonstrated that the distortion removal and false positive filtering are effective to improve the position estimation precision and the manipulation success rate.

• Cartoon and Animation Studies
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• s.35
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• pp.347-374
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• 2014

The aim of this article is to consider the future of industrialization of Augmented Reality contents focusing on cinematic imagination of films that used Augmented Reality techniques and artistic imagination of Augmented Reality Arts in the 21st century. The film showing future technology through cinematic imagination plays an role in the presentation of future vision important. Augmented Reality Arts show the big picture of future arts, future aspect of society, and future culture by using technically possible present technology. I classified the researched films according to Augmented Reality technique. It is expected that Gesture Recognition will develop with transparent display device techniques, Hologram techniques will be changed into individualized communication styles, Biometrics will be able to evolve into multi-Biometrics, and Wearable Computer will develop in the aspect of physical body augmentation and then industrialize. In Augmented Reality Arts, it seems that the various utilization of avatar will be related to Hologram, the utilization of the physiological phenomenon of the human body will be related to Biometrics, the mixture of reality and virtual reality will utilize display devices through Gesture Recognition, and a new experiment of HMD(Head-Mounted Display) will industrialize with the diversification of Wearable Computer. Augmented Reality contents created through the imagination and representation in the films and arts take a role in helping human life, and, at the same time, show the future image industrialized in the way of combination between human and environment without a medium.

• Journal of The Korean Association of Information Education
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• v.9 no.2
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• pp.243-254
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• 2005

Along the information technology progresses, the demand for the educational application increases which derives the need for cyber education. Cyber education should take the knowledge construction model rather than the knowledge delivery model. For the educational paradigm shift by cyber education, 'Project-Based Learning' and 'Cyber Learning Community' need to be adopted. In this study, project-based cyber learning community for teacher education is developed with 5 major categories: Home(community vision, general information, project list, Q&A), project learning center(preparation for the instructional activities, group-based learning activities, presentations), meeting plaza(request for group mentoring, sharing ideas, meeting specialist, survey participating, forum participating, result presentation), references(cases of project studies, materials for project studies, materials for teacher education, general materials), management(project management, project cases, idea bank, finding partners, dialog with instructors, sharing references, sharing experiences, survey, forum).