• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.76-83
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• 2023

Recently, the development of computer vision with deep learning has made object detection using images applicable to diverse fields, such as medical care, manufacturing, and transportation. The manufacturing industry is saving time and money by applying computer vision technology to detect defects or issues that may occur during the manufacturing and inspection process. Annotations of collected images and their location information are required for computer vision technology. However, manually labeling large amounts of images is time-consuming, expensive, and can vary among workers, which may affect annotation quality and cause inaccurate performance. This paper proposes a process that can automatically collect annotations and location information for images using eXplainable AI, without manual annotation. If applied to the manufacturing industry, this process is thought to save the time and cost required for image annotation collection and collect relatively high-quality annotation information.

• Journal of Sensor Science and Technology
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• v.15 no.1
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• pp.20-27
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• 2006

This paper presents a gas measurement system for deciding hole positions on a PU middle-sole mold from computed gas amount. The optimal number of holes and their positions on the shoe mold are decided from statistical experiment results to overcome the problem of excessive expenses in gas vent exchange. This paper also describes a gas vent exchange mechanism using computer vision system. The gas hole detecting process is based on computer vision algorithms represented as a simple Pattern Matching. The experimental result showed us that the system was useful to calculate the number of holes and their positions on the shoes mold.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.68-73
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• 2005

Various techniques have been proposed for detection and tracking of targets in order to develop a real-world computer vision system, e.g., visual surveillance systems, intelligent transport systems (ITSs), and so forth. Especially, the idea of distributed vision system is required to realize these techniques in a wide-spread area. In this paper, we develop a ubiquitous vision system for location-awareness of multiple targets. Here, each vision sensor that the system is composed of can perform exact segmentation for a target by color and motion information, and visual tracking for multiple targets in real-time. We construct the ubiquitous vision system as the multiagent system by regarding each vision sensor as the agent (the vision agent). Therefore, we solve matching problem for the identity of a target as handover by protocol-based approach. We propose the identified contract net (ICN) protocol for the approach. The ICN protocol not only is independent of the number of vision agents but also doesn't need calibration between vision agents. Therefore, the ICN protocol raises speed, scalability, and modularity of the system. We adapt the ICN protocol in our ubiquitous vision system that we construct in order to make an experiment. Our ubiquitous vision system shows us reliable results and the ICN protocol is successfully operated through several experiments.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.2
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• pp.251-256
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• 2017

This study developed an image processing system for detecting damages on underground spiral PVC pipes. The detection method is simple-identifying damaged areas by measuring circularity along the pipeline. This uses the assumption that damage parts will not make a circular shape. Conventional devices check the circular shape of the pipe along the pipeline by measuring the angles between 6 spring-connected legs on the device. The conventional device, however, requires the insertion of 3 different wires (electrical, communication, and camera lines) along with a guide wire for pulling the device. The developed system presented here has simplified this system, requiring only a camera line while maintaining reasonable accuracy in damage detection.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.119-128
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• 2020

Object detection is an important field of computer vision and is applied to applications such as security, autonomous driving, and face recognition. Recently, as the application of artificial intelligence technology including deep learning has been applied in various fields, it has become a more powerful tool that can learn meaningful high-level, deeper features, solving difficult problems that have not been solved. Therefore, deep learning techniques are also being studied in the field of object detection, and algorithms with excellent performance are being introduced. In this paper, a deep learning-based object detection algorithm used to detect multiple objects in an image is investigated, and future development directions are presented.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.7-11
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• 2022

Object detection technology is one of the main research topics in the field of computer vision and has established itself as an essential base technology for implementing various vision systems. Recent DNN (Deep Neural Networks)-based algorithms achieve much higher recognition accuracy than traditional algorithms. However, it is well-known that the DNN model inference operation requires a relatively high computational power. In this paper, we analyze the inference time complexity of the state-of-the-art object detection architecture Yolov7 in various environments. Specifically, we compare and analyze the time complexity of four types of the Yolov7 model, YOLOv7-tiny, YOLOv7, YOLOv7-X, and YOLOv7-E6 when performing inference operations using CPU and GPU. Furthermore, we analyze the time complexity variation when inferring the same models using the Pytorch framework and the Onnxruntime engine.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.516-519
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• 1999

Gaze detection is to find out the position on a monitor screen where a user is looking at, using the image processing and computer vision technology, We developed a computer interface system using the gaze detection technology, This system enables a user to control the computer system without using their hands. So this system will help the handicapped to use a computer and is also useful for the man whose hands are busy doing another job, especially in tasks in factory. For the practical use, command signal like mouse clicking is necessary and we used eye winking to give this command signal to the system.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.738-747
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• 2021

Today, with the improvement of deep learning technology, computer vision areas such as image classification, object detection, object segmentation, and object tracking have shown remarkable improvements. Various applications such as intelligent surveillance, robots, Internet of Things, and autonomous vehicles in combination with deep learning technology are being applied to actual industries. Accordingly, the requirement of an efficient compression method for video data is necessary for machine consumption as well as for human consumption. In this paper, we propose an object-based compression of thermal infrared images for machine vision. The input image is divided into object and background parts based on the object detection results to achieve efficient image compression and high neural network performance. The separated images are encoded in different compression ratios. The experimental result shows that the proposed method has superior compression efficiency with a maximum BD-rate value of -19.83% to the whole image compression done with VVC.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.6
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• pp.110-116
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• 2007

This paper presents a new technique to implement the real-time recognition for shapes and model number of parts based on an active vision approach. The main focus of this paper is to apply a technique of 3D object recognition for non-contacting inspection of the shape and the external form state of precision parts based on the pattern recognition. In the field of computer vision, there have been many kinds of object recognition approaches. And most of these approaches focus on a method of recognition using a given input image (passive vision). It is, however, hard to recognize an object from model objects that have similar aspects each other. Recently, it has been perceived that an active vision is one of hopeful approaches to realize a robust object recognition system. The performance is illustrated by experiment for several parts and models.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.151-162
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• 2021

Quantum information has passed the theoretical research period and has entered the realization step for its application to the information and communications technology (ICT) sector. Currently, quantum information has the advantage of being safer and faster than conventional digital computers. Thus, a lot of research is being done. The amount of big data that one needs to deal with is expected to grow exponentially. It is also a new business model that can change the landscape of the existing computing. Just as the IT sector has faced many challenges in the past, we need to be prepared for change brought about by Quantum. We would like to look at studies on quantum communication, quantum sensing, and quantum computing based on quantum information and see the technology levels of each country and company. Based on this, we present the vision and challenge for quantum information in the future. Our work is significant since the time for first-time study challengers is reduced by discussing the fundamentals of quantum information and summarizing the current situation.