• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.75-80
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• 2008

This paper presents a vision based autonomous inspection system for welding quality control of a RV sinking seat. In order to overcome the precision error that arises from a visible inspection by an operator in the manufacturing process of a RV sinking seat, the machine vision based welding quality control system is proposed. It consists of the CMOS camera and the NI vision system. The geometry of the welding bead, which is the welding quality criteria, is measured by using the captured image with a median filter applied on it. The image processing software for the system was developed using the NI LabVIEW software. The proposed welding quality inspection system for RV sinking seat was verified using experimentation.

• Journal of Welding and Joining
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• v.19 no.1
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• pp.88-94
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• 2001

DUPIC (Direct use of spent PWR fuel in CANDU reactors) nuclear fuel is a CANDU fuel fabricated remotely from spent PWR fuel materials in a hot cell. The soundness of the end closure welds of nuclear fuel elements is an important factor for the safety and performance of nuclear fuel. To evaluate the soundness of the end closure welds of DUPIC fuel element, a precise X-ray inspection system is developed using a micro-focus X-ray generator with an image intensifier and a real time camera system. The fuel elements made of Zircaloy-4 and stainless steel by an Nd:YAG laser welding and a TIG welding aye inspected by the developed inspection system. The soundness of the welds of the fuel elements was confirmed by the X-ray inspection process, and the irradiation test of DUPIC fuel elements has been successfully completed at the HANARO research reactor.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.5
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• pp.58-65
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• 2016

Recent frequent occurrence of urban sinkhole serves as a momentum of the periodic inspection of sewer pipelines. Sewer inspection using a CCTV device needs a lot of time and efforts. Many of previous studies which reduce the laborious tasks are mainly interested in the developments of image processing S/W and inspection H/W. However there has been no attempt to find meaningful information from the existing CCTV images stored by the sewer maintenance manager. This study adopts a cross-correlation based image processing method and extracts location data of sewer inspection device from CCTV images. As a result of the analysis of time-location relation, it shows strong correlation between the device's stand times and the sewer damages. In case of using this method to investigate sewer inspection CCTV images, it will save the investigator's efforts and improve the sewer maintenance efficiency and reliability.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.425-427
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• 2006

In this paper, an automatic side-view inspection algorithm for LCD modules is proposed. Until now, most parts of inspection is performed by human inspectors, which means very high product costs. So inspection automation is the very hot issue in the LCD industries. However, it is not easy problem to replace the human by computer vision system. In the many inspections which are based on the human eyes, side-view inspection is most hard problem to solve. In this paper, an image morphing algorithm is developed, which will help to enable the automation of the side-view inspection process.

• The monthly packaging world
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• s.87
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• pp.176-181
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• 2000

• The monthly packaging world
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• s.291
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• pp.71-76
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• 2017

• Journal of the Korean Society for Precision Engineering
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• v.12 no.10
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• pp.78-88
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• 1995

For producing iron or steel products with good quality, the concentration of the material components should be analyzed quickly with high relability using XRF(Fluorescent X-Ray Spectrometer). Since the analysis results are much dependent upon the surface con- dition, the samples have to be prepared to have good test condition. This study presents an image processing system for inspecting the surface condition of the iron test sample. In order to use thd computer vision system, we need to develop a lighting device and image processing algorithm. For the adequate lighting device of inspection system, the indirect lighting device is contrived to cut the external light and provide uniform, stable and cold light. The image processing algorithm is aimed to reduce inspection time and to get similar analyzing results to those of the experienced operators. At first, the image processing algorithm checks whether the surface of the iron sample is ground well or not. Then, the defects; hole or dig are conted and surface condition is evaluated. In addition, the algorithm gives the reliability of the analyzing results in order to help operator's decision.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.04a
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• pp.343-348
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• 1992

Using the real-time image processing technique, we have developed an automatic visual inspection system which detects the defects of the surface muonted components in PCB( missing components, mislocation, mismounts, and reverse polarity, etc ) and collects the quality control and production management data. An image processing system based on a commercial parallel processor, TRANSPUTER by which the image processing time can be largely reduced was designed. Analyzing the collected data, the proposed inspection system contributes to the productivity improvement throughthe reduction of defective rate.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3056-3059
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• 2000

This paper describes a fast image labeling algorithm for the feature extraction of connected components. Labeling the connected regions of a digitized image is a fundamental computation in image analysis and machine vision, with a large number of application that can be found in various literature. This algorithm is designed for the surface defect inspection of Cold Mill Strip. The labeling algorithm permits to separate all of the connected components appearing on the Cold Mill Strip.

• 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.