• Journal of Industrial Technology
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• v.21 no.B
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• pp.67-74
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• 2001

According as the patterns of PCB (Printed Circuit Board) become denser and complicated, quality and accuracy of PCB influence the performance of final product. It's attempted to obtain trust of 100% about all of parts. Because human inspection in mass-production manufacturing facilities are both time-consuming and very expensive, the automation of visual inspection has been attempted for many years. Thus, automatic visual inspection of PCB is required. In this paper, we used an algorithm which compares the reference PCB patterns and the input PCB patterns are separated an object and a scene by filtering and edge detection. And than compare two image using pattern matching algorithm. We suggest an defect inspection algorithm in PCB pattern, to be satisfied low cost, high speed, high performance and flexibility on the basis of $640{\times}480$ binary pattern.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.281-285
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• 1996

In this paper, I made visual inspection system using Vision Board and it is consist of an illuminator (a fluorescent lamp), image input device(CCD(Charge)Coupled Device) camera), image processing system(Vision Board(FARAMVB-02), image output device(videomonitor, printer), a measuring instrument(TELMN1000). Length measurement by visual inspection system is used 100mm gauge block instead of calculating distance between camera and object, it measured horizontal and vertical length factor from 400mm to 650mm by increasing 50mm. In this place, measured horizontal and vertical length factor made use of length measurement of a injection. A measuring instrument used to compare a measured length of a injection visual inspection system with it. In conclusion, length measurement of a injection compared a measuring instrument withvisual inspecion system using length factor of 100mm guage block. Maximum error of length compared two devices a measuring instrument with visual inspection system is 0.55mm. And operation program is made up Borland C++ 3.1. By changing, it is applied to various uses.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.1
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• pp.54-59
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• 2004

We suggest a 3D vision inspection algorithm which is based on the external shape feature. Because many electronic parts have the regular shape, if we have the database of pattern and can recognize the object using the database of the object s pattern, we can inspect many types of electronic parts. Our proposed algorithm uses the geometrical pattern matching method and 3D database on the electronic parts. We applied our suggested algorithm fer inspecting several objects including typical IC and capacitor. Through the experiments, we could find that our suggested algorithm is more effective and more robust to the inspection environment(rotation angle, light source, etc.) than conventional 2D inspection methods. We also compared our suggested algorithm with the feature space trajectory method.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1109-1114
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• 2015

This paper presents an improved learning-based visual inspection method for auto parts inspection in severe lighting changes. Automobile sunroof frames are produced automatically by robots in most production lines. In the sunroof frame manufacturing process, there is a quality problem with some parts such as volts are missed. Instead of manual sampling inspection using some mechanical jig instruments, a learning-based machine vision system was proposed in the previous research[1]. But, in applying the actual sunroof frame production process, the inspection accuracy of the proposed vision system is much lowered because of severe illumination changes. In order to overcome this capricious environment, some selective feature vectors and cascade classifiers are used for each auto parts. And we are able to improve the inspection accuracy through the re-learning concept for the misclassified data. The effectiveness of the proposed visual inspection method is verified through sufficient experiments in a real sunroof production line.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.123-124
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• 2008

The appearance inspection of various electronic products and parts has been executed by the eyesight of human. But inspection by eyesight can't bring about uniform inspection result. Because the appearance inspection result by eyesight of human is changed by condition of physical and spirit of the checker. So machine vision inspection system is currently used to many appearance inspection fields instead of the checker. Therefore we proposes a inspection system in this paper. it will be able to secure the objectivity of the prosecuting attorney using inspection system. Also this system has been developed only using PC, CCD Camera and Visual C++ for universal workplace.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.6
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• pp.1157-1162
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• 2001

This paper deals with inspect algorithm using visual system. One of the major problems that arise during polymer production is the estimation of the noise of the color product.(bad pallets) An erroneous output can cause a lot of losses (production and financial losses). Therefore new methods for real-time inspection of the noise are demanded. For this reason, we have presented a development of vision system algorithm for the defect inspection of PE color pallets. First of all, in order to detect the edge of object, the differential filter is used. And we apply to the labelling algorithm for feature extraction. This algorithm is designed for the defect inspection of pallets. The labelling algorithm permits to separate all of the connected components appearing on the pallets. Labelling the connected regions of a image is a fundamental computation in image analysis and machine vision, with a large number of application. Also, we suggested vision processing program in window environment. Simulations and experimental results demonstrate the performance of the proposal algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.587-591
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• 2001

This paper deals with inspect algorithm using visual system. One of the major problems that arise during polymer production is the estimation of the noise of the color product.(bad pallets) An erroneous output can cause a lot of losses (production and financial losses). Therefore new methods for real-time inspection of the noise are demanded. For this reason, we have presented a development of vision system algorithm for the defect inspection of PE color pallets. First of all, in order to detect the edge of object, the differential filter is used. And we apply to the labelling algorithm for feature extraction. This algorithm is designed for the defect inspection of pallets. The labelling algorithm permits to separate all of the connected components appearing on the pallets. Labelling the connected regions of a image is a fundamental computation in image analysis and machine vision, with a large number of application. Also, we suggested vision processing program in window environment. Simulations and experimental results demonstrate the performance of the proposal algorithm.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.2
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• pp.67-74
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• 2003

This paper presents the development of the Inspection machine for SMD mounted on cream solder of PCB. There are mounting errors of SMD such as misalignment, missing part, wrong orientation, wrong polarity and so on. The main hardware of the system consists of a machine vision part and a motion control part. Operating software has been developed in GUI environment to help user convenience. The Inspection Jobs consist of two procedures, that is, creation of the inspection reference data and automatic inspection. The Inspection reference data has a tree structure of linked list including PCB information, blocks, components, windows, and inspection methods. This paper presents versatile inspection methods which include a section length method, a projection method and histogram method. Therefore, user can choose the suitable procedure for various components. Finally, the automatic Inspection procedure using the reference data checks the mounting errors of components.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.6
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• pp.491-497
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• 2016

This study aims to develop a vision inspection system for screw threads. To inspect external defects in screw threads, the vision inspection system was developed using front light illumination from which bright images can be obtained. The front light system, however, requires multiple side images for inspection of the entire thread surface, which can be performed by omnidirectional optics. In this study, an omnidirectional optical system was designed to obtain annular images of screw threads using an image sensor and two reflection mirrors; one large concave mirror and one small convex mirror. Optical simulations using backward and forward ray tracing were performed to determine the dimensional parameters of the proposed optical system, so that an annular image of the screw threads could be obtained with high quality and resolution. Microscale surface defects on the screw threads could be successfully detected using the developed annular inspection system.