• Title/Summary/Keyword: Machine vision

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Image Enhanced Machine Vision System for Smart Factory

  • Kim, ByungJoo
    • 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.

An Automated Machine-Vision-based Feeding System for Engine Mount Parts (머신비젼 기반의 엔진마운트 부품 자동공급시스템)

  • Lee, Hyeong-Geun;Lee, Moon-Kyu
    • Journal of the Korean Society for Precision Engineering
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    • v.18 no.5
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    • pp.177-185
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    • 2001
  • This paper describes a machine-vision-based prototype system for automatically feeding engine-mount parts to a swaging machine which assembles engine mounts. The system developed consists of a robot, a feeding device with two cylinders and two photo sensors, and a machine vision system. The machine vision system recognizes the type of different parts being fed from the feeding device and estimates the angular difference between the inner-hole center of the part and the point predetermined for assembling. The robot then picks up each part and rotated it through the estimated angle such that the parts are well assembled together as specified. An algorithm has been developed to recognize different part types and estimate the angular difference. The test results obtained for a set of real specimens indicate that the algorithm performs well enough to be applied to prototype system.

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Monitoring of Micro-Drill Wear by Using the Machine Vision System (머신비전 시스템을 이용한 마이크로드릴 마멸의 상태감시)

  • Choi Young-Jo;Chung Sung-Chong
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.30 no.6
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    • pp.713-721
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    • 2006
  • Micro-drill wear deteriorates accuracy and productivity of the micro components. In order to improve productivity and qualify of micro components, it is required to investigate micro-drill wear exactly. In this study, a machine vision system is proposed to measure the wear of micro-drills using a precision servo stage. Calibration experiments are conducted to compensate for the machine vision system. In this paper, worn volume, area and length are defined as wear amounts. Micro-drill wear is reconstructed as the 3D topography and the quantized wear amount by using the shape from focus (SFF) method and wear parameters. Experiments have been conducted with HSS twist micro-drills and SM45C carbon steel workpieces. Validity of the proposed machine vision system is confirmed through experiments.

Quantization and Calibration of Color Information From Machine Vision System for Beef Color Grading (소고기 육색 등급 자동 판정을 위한 기계시각 시스템의 칼라 보정 및 정량화)

  • Kim, Jung-Hee;Choi, Sun;Han, Na-Young;Ko, Myung-Jin;Cho, Sung-Ho;Hwang, Heon
    • Journal of Biosystems Engineering
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    • v.32 no.3
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    • pp.160-165
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    • 2007
  • This study was conducted to evaluate beef using a color machine vision system. The machine vision system has an advantage to measure larger area than a colorimeter and also could measure other quality factors like distribution of fats. However, the machine vision measurement is affected by system components. To measure the beef color with the machine vision system, the effect of color balancing control was tested and calibration model was developed. Neural network for color calibration which learned reference color patches showed a high correlation with colorimeter in L*a*b* coordinates and had an adaptability at various measurement environments. The trained network showed a very high correlation with the colorimeter when measuring beef color.

A Study on the Elliptical Gear Inspection System Using Machine Vision (머신비전을 이용한 타원형 기어 검사 시스템에 관한 연구)

  • Park, Jin Joo;Kim, Gi Hwan;Lee, Eung Seok
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.38 no.1
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    • pp.59-63
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    • 2014
  • Elliptical gears are used in the oval flowmeter and oval flow meter inspects volume of water thanks to space by the elliptical shape. The purpose of this study is to judge accuracy of processing of the elliptical gear and develop inspection system using machine vision. Demand of machine vision is increasing while the factory automation is spreading and principle factor in-process inspection. But, gear inspection using the machine vision rarely used because of complex shape of gear. In this study, it seems possible that elliptical gear is inspected by inspection software using machine vision and inspection program can judge accuracy of processing of the elliptical gear designed this study.

Selection of Apple Ground Color for Maturity Index Using Color Machine Vision (컬러 컴퓨터 시각에 의한 사과 선별 기준색깔 선정)

  • 서상룡;성제훈
    • Journal of Biosystems Engineering
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    • v.22 no.2
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    • pp.210-216
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    • 1997
  • A study to select ground colors of Fuji apple for maturity index which are needed to standardize grading of the apples is presented. Two extreme colors of immature and fully mature Fuji and Zonagold apples produced in Korea were determined. Various ground colors of Fuji apple between the two extreme colors were collected and classified by human vision and colors of Fuji apple for maturity index were selected from the classification. Coordinates of the selected colors in xy chromaticity diagram were determined by spectrophotometers to define them in a standard coordinate system. Coordinates of the colors in r-g chromaticity diagram using a color machine vision system were also determined to use the colors in apple grading by the machine vision system. Grading Fuji apples using the machine vision system was performed and result of the grading was compared with Ending results of human vision and colorimeter. The comparison was performed with the same Fuji apple samples and showed 65% md 75% of same grades, respectively, as the grades determined by the machine vision system. Differences of fading performance between the compared three grading methods were explained as mainly because of the differences of observation area of the grading methods.

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Deep Learning Machine Vision System with High Object Recognition Rate using Multiple-Exposure Image Sensing Method

  • Park, Min-Jun;Kim, Hyeon-June
    • Journal of Sensor Science and Technology
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    • v.30 no.2
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    • pp.76-81
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    • 2021
  • In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

Development of Machine Vision System based on PLC (PLC 기반 머신 비전 시스템 개발)

  • Lee, Sang-Back;Park, Tae-Hyoung;Han, Kyung-Sik
    • Journal of Institute of Control, Robotics and Systems
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    • v.20 no.7
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    • pp.741-749
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    • 2014
  • This paper proposes a machine vision module for PLCs (Programmable Logic Controllers). PLC is the industrial controller most widely used in factory automation system. However most of the machine vision systems are based on PC (Personal Computer). The machine vision system embedded in PLC is required to reduce the cost and improve the convenience of implementation. In this paper, we newly propose a machine vision module based on PLC. The image processing libraries are implemented and integrated with the PLC programming tool. In order to interface the libraries with ladder programming, the ladder instruction set was also designed for each vision library. By use of the developed system, PLC users can implement vision systems easily by ladder programming. The developed system was applied to sample inspection system to verify the performance. The experimental results show that the proposed system can reduce the cost of installing as well as increase the ease-of-implementation.

Calibration for Color Measurement of Lean Tissue and Fat of the Beef

  • Lee, S.H.;Hwang, H.
    • Agricultural and Biosystems Engineering
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    • v.4 no.1
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    • pp.16-21
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    • 2003
  • In the agricultural field, a machine vision system has been widely used to automate most inspection processes especially in quality grading. Though machine vision system was very effective in quantifying geometrical quality factors, it had a deficiency in quantifying color information. This study was conducted to evaluate color of beef using machine vision system. Though measuring color of a beef using machine vision system had an advantage of covering whole lean tissue area at a time compared to a colorimeter, it revealed the problem of sensitivity depending on the system components such as types of camera, lighting conditions, and so on. The effect of color balancing control of a camera was investigated and multi-layer BP neural network based color calibration process was developed. Color calibration network model was trained using reference color patches and showed the high correlation with L*a*b* coordinates of a colorimeter. The proposed calibration process showed the successful adaptability to various measurement environments such as different types of cameras and light sources. Compared results with the proposed calibration process and MLR based calibration were also presented. Color calibration network was also successfully applied to measure the color of the beef. However, it was suggested that reflectance properties of reference materials for calibration and test materials should be considered to achieve more accurate color measurement.

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Development of a System to Measure Quality of Cut Flowers of Rose and Chrysanthemum Using Machine Vision (기계시각을 이용한 장미와 국화 절화의 품질 계측장치 개발)

  • 서상룡;최승묵;조남홍;박종률
    • Journal of Biosystems Engineering
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    • v.28 no.3
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    • pp.231-238
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    • 2003
  • Rose and chrysanthemum are the most popular flowers in Korean floriculture. Sorting flowers is a labor intensive operation in cultivation of the cut flowers and needed to be mechanized. Machine vision is one of the promising solutions for this purpose. This study was carried out to develop hardware and software of a cut flower sorting system using machine vision and to test its performance. Results of this study were summarized as following; 1. Length of the cut flower measured by the machine vision system showed a good correlation with actual length of the flower at a level of the coefficients of determination (R$^2$) of 0.9948 and 0.9993 for rose and chrysanthemum respectively and average measurement errors of the system were about 2% and 1% of the shortest length of the sample flowers. The experimental result showed that the machine vision system could be used successfully to measure length of the cut flowers. 2. Stem diameter of the cut flowers measured by the machine vision system showed a correlation with actual diameter at the coefficients of determination (R$^2$) of 0.8429 and 0.9380 for rose and chrysanthemum respectively and average measurement errors of the system were about 15% and 7.5% of the shortest diameter of the sample flowers which could be a serious source of error in grading operation. It was recommended that the error rate should be considered to set up grading conditions of each class of the cut flowers. 3. Bud maturity of 20 flowers each judged using the machine vision system showed a coincidence with the judgement by inspectors at ranges of 80%∼85% and 85%∼90% for rose and chrysanthemum respectively. Performance of the machine vision system to judge bud maturity could be improved through setting up more precise criteria to judge the maturity with more samples of the flowers. 4. Quality of flower judged by stem curvature using the machine vision system showed a coincidence with the judgement by inspectors at 90% for good and 85% for bad flowers of both rose and chrysanthemum. The levels of coincidence was considered as that the machine vision system used was an acceptable system to judge the quality of flower by stem curvature.