• Journal of Korea Multimedia Society
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• v.18 no.5
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• pp.583-598
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• 2015

We need many assembly lines to produce industrial product such as automobiles that require a lot of composited parts. Big portion of such assembly line are still operated by manual works of human. Such manual works sometimes cause critical error that may produce artifacts. Also, once the assembly is completed, it is really hard to verify whether of not the product has some error. In this paper, for monitoring behaviors of manual human work in an assembly line automatically, we proposes a realtime hybrid monitoring system that combines 2D vision sensor tracking technique with 3D motion recognition sensors.

• Advances in robotics research
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• v.1 no.1
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• pp.81-99
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• 2014

This paper contributes towards the development of a computer vision system for telemonitoring of industrial articulated robotic arms. The system aims to provide precision real time measurements of the joint angles by employing low cost cameras and visual markers on the body of the robot. To achieve this, a mathematical model that connects image features and joint angles was developed covering rotation of a single joint whose axis is parallel to the visual projection plane. The feature that is examined during image processing is the varying area of given circular target placed on the body of the robot, as registered by the camera during rotation of the arm. In order to distinguish between rotation directions four targets were used placed every $90^{\circ}$ and observed by two cameras at suitable angular distances. The results were deemed acceptable considering camera cost and lighting conditions of the workspace. A computational error analysis explored how deviations from the ideal camera positions affect the measurements and led to appropriate correction. The method is deemed to be extensible to multiple joint motion of a known kinematic chain.

• Proceedings of the KIEE Conference
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• 2002.06a
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• pp.41-45
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• 2002

In industrial computer vision systems, the image quality is dependent on the parameters such as light source, illumination method, optics, and surface properties. Most of them are related with the lighting system, which is designed in heuristic, based on the designer's experimental knowledge, In this paper, a design code by which the optimal lighting method and light source for computer vision systems can be found are suggested based on experimental results, To prove the usefulness of the design code, it is applied to the lighting system design of the transistor marking inspection system and the results are presented.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.10 no.2
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• pp.67-80
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• 2006

In this paper, we propose a new pointing device that is replaced a mouse as the pointing device with. For reducing the existing pointing device's problem which had marker and high-cost, we develop a new pointing device using computer vision like as a similar human vision system. The proposed system first carries out a real-time movement tracking system using image data which are segmented by color modeling, and finally does the pointing action by 3-D coordinate calculated from stereo geometry information resulting from stereo matching of the segmented region.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.7-13
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• 2021

Machine vision is a technology that helps the computer as if a person recognizes and determines things. In recent years, as advanced technologies such as optical systems, artificial intelligence and big data advanced in conventional machine vision system became more accurate quality inspection and it increases the manufacturing efficiency. In machine vision systems using deep learning, the image quality of the input image is very important. However, most images obtained in the industrial field for quality inspection typically contain noise. This noise is a major factor in the performance of the machine vision system. Therefore, in order to improve the performance of the machine vision system, it is necessary to eliminate the noise of the image. There are lots of research being done to remove noise from the image. In this paper, we propose an autoencoder based machine vision system to eliminate noise in the image. Through experiment proposed model showed better performance compared to the basic autoencoder model in denoising and image reconstruction capability for MNIST and fashion MNIST data sets.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.499-503
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• 2004

This application-oriented paper describes an automated welding carriage system to weld a thin corrugated workpiece with welding seam tracking function. Hyundai Heavy Industries Corporation has developed an automatic welding carriage system, which utilizes pulsed plasma arc welding process for corrugated sheets. It can obtain high speed welding more than 2 times faster than traditional TIG based welding system. The aim of this development is to increase the productivity by using automatic plasma welding carriage systems, to track weld seam line using vision sensor automatically, and finally to provide a convenience to operator in order to carry out welding. In this paper a robust image processing and a distance based tracking algorithms are introduced for corrugated workpiece welding. The automatic welding carriage system is controlled by the programmable logic controller(PLC), and the automatic welding seam tracking system is controlled by the industrial personal computer(IPC) equipped with embedded OS. The system was tested at actual workpiece to show the feasibility and performance of proposed algorithm and to confirm the reliability of developed controller.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.9
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• pp.1531-1540
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• 1997

Mordern industrial society pursues unmanned system and automation of manufacturing process. Abreast with this tendensy, production of goods which requires advanced accuracy is increasing as well. According to this, the work sensing time of dressing by monitoring and diagnosing the condition of grinding, which is th representative way in accurate manufacturing, is an important work to prevent serious damages which affect grinding process or products by wearing grinding wheel. Computer vision system was composed, so that grinding wheel surface was acquired by CCD camera and the change of cutting edge ratio was measured. Then we used automatic thresholding technique from histogram as a way of dividing grinding cutting edge from grinding surface. As a result, we are trying to approach unmanned system and automation by deciding more accurate time of dressing and by visualizing behavior of grinding wheel by making use of computer vision.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.11
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• pp.18-27
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• 2015

In this paper, we proposed the spinning right-angle stereo vision system to center the shifted object on 3-dimensional image using a human eyesight-like, and the system is reconstructed with conventional stereo vision system. In this proposed system, the centering results of objects on the 3-dimensional image are very good, and we got the parameter ratios 89~112% for the real measurement values. Therefore, the suggested the spinning right-angle stereo vision system have a high possibilities to be applied to many industrial system parts and to be used for robot system, automatic system, and etc.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.37-38
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• 2023

본 논문에서는 딥러닝을 기반으로 하는 키포인트 인식 모델을 산업용 품질검사 머신비전에 응용하는 방법을 제안한다. 전이학습 방법을 이용하여 딥러닝 모델의 인식률을 높이는 방법을 제시하였고, 전이시킨 특성 추출 모델에 대해 추가로 데이터 세트에 대한 학습을 진행하는 것이 특성추출 모델의 초기 ImageNet 가중치를 동결시켜 학습하는 것보다 학습 속도나 정확도가 높다는 것을 보여준다. 실험을 통해 딥러닝을 응용하는 산업용 품질 검사 공정에는 특성추출 모델의 추가 학습이 중요하다는 점을 확인할 수 있었다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.1
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• pp.9-19
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• 2024

Deep learning-based computer vision anomaly detection algorithms are widely utilized in various fields. Especially in the manufacturing industry, the difficulty in collecting abnormal data compared to normal data, and the challenge of defining all potential abnormalities in advance, have led to an increasing demand for unsupervised learning methods that rely on normal data. In this study, we conducted a comparative analysis of deep learning-based unsupervised learning algorithms that define and detect abnormalities that can occur when transparent contact lenses are immersed in liquid solution. We validated and applied the unsupervised learning algorithms used in this study to the existing anomaly detection benchmark dataset, MvTecAD. The existing anomaly detection benchmark dataset primarily consists of solid objects, whereas in our study, we compared unsupervised learning-based algorithms in experiments judging the shape and presence of lenses submerged in liquid. Among the algorithms analyzed, EfficientAD showed an AUROC and F1-score of 0.97 in image-level tests. However, the F1-score decreased to 0.18 in pixel-level tests, making it challenging to determine the locations where abnormalities occurred. Despite this, EfficientAD demonstrated excellent performance in image-level tests classifying normal and abnormal instances, suggesting that with the collection and training of large-scale data in real industrial settings, it is expected to exhibit even better performance.