• Title/Summary/Keyword: Visual Inspection System

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Implementation of an Automated Visual Inspection System for the Junction Box (정션박스 자동 시각 검사 시스템 구현)

  • Heo Seheung;Hahn Kwangsoo;Choi JoonSoo;Lee Hojun
    • Proceedings of the Korean Information Science Society Conference
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    • 2005.07b
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    • pp.862-864
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    • 2005
  • 자동차 전자 부품 중 하나인 정션박스의 자동 시각 검사 시스템은 사랑의 육안 검사를 자동화함으로써, 정확하고 빠른 검사가 가능하다. 또한 검사 결과를 데이터베이스화하여 지속적인 불량 발생요인을 분석할 수 있기 때문에 불량률을 감소시켜 제품 신뢰도를 향상시킬 수 인다. 본 연구에서는 정션박스에 삽입된 퓨즈와 릴레이의 유무 및 오결합을 자동으로 검사하는 자동 시각 검사 시스템을 구현하였고 이를 적용한 결과 신속하고 정확한 검사가 가능함을 보였다.

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The New Estimation of Surface Discharge Insulation Using Fractal Dimension (프랙탈 차원을 이용한 SD절연의 새로운 평가)

  • Lim, Jang-Seob;Han, Jae-Hong;Kim, Duck-Keun
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • 2000.05a
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    • pp.55-58
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    • 2000
  • Fractal mathematics is being highlighted as a research method for classification of image. But the application of Fractal dimension(FD) has been required the complicated calculation method because of its complex repetition progressing. In this paper, it has been developed the new approach method to express the Fractal Dimension(FD) for aging level calculation and estimation system of outside insulator using special image processing algorithm. As a result after FD testing, the recognized aging estimation of FD has a very characteristics compared to the conventional visual inspection.

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Development of Visual Inspection System to the defect of Quad chip (Quad chip의 외관 불량 검사 시스템 개발)

  • Lee, Ji Yeon;Ko, Kuk Won;Han, Chang Ho
    • Proceedings of the Korea Information Processing Society Conference
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    • 2015.10a
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    • pp.1076-1077
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    • 2015
  • 본 연구에서는 최근 널리 사용되고 있는 QFP(Quad Flat Package)의 소형화 및 대량 생산 Quad chip 공정에서 최종 외관 불량 검사를 위한 기존의 2D 영상 검사 시스템에 3D 영상 검사 시스템을 추가하여 광학 장치를 설계하고 이에 따른 영상처리 알고리즘을 개발하였다. 개발된 검사 장치는 실제 LQFP/TQFP에 생산 공정에 적용되어 불량을 검사에 적용하였으며, 10 회 반복 측정 시 최대 오차는 $1.34{\mu}m$와 측정 오차의 표준편차가 $0.715{\mu}m$으로 요구하는 3차원 불량 검사를 만족할 만한 성능을 보였다.

Trial Installation and Performance Evaluation of Prefabricated Concrete Slab Track on Revenue Line (프리캐스트 콘크리트 슬래브궤도의 영업선 시험시공 및 성능평가)

  • Jang, Seung-Yup;Kang, Yun-Suk;Lee, Hu-Sam;Kim, Yu-Bong;Lee, Jong-Soo
    • Proceedings of the KSR Conference
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    • 2008.11b
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    • pp.840-845
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    • 2008
  • To develop our original technology of concrete slab track, being widely accepted for new track, prefabricated concrete slab track, or precast concrete slab track has been developed. They have two different types according to slab shape and its dimensions, connection of slabs and connecting structure onto substructure. After the system design and successful performance evaluation in the laboratory, the trial installation on revenue line has been carried out. This paper is presenting the result of the trial installation and the performance tests in field. The performance tests have been performed as visual inspection for cracks and damages, measurement of track alignment and elastic behavior of track under passing trains. The performance test results during last 2 years have shown that no remarkable damages and settlements were found, and track alignment and elastic track behavior both exhibits good status.

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Railroad Surface Defect Segmentation Using a Modified Fully Convolutional Network

  • Kim, Hyeonho;Lee, Suchul;Han, Seokmin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.12
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    • pp.4763-4775
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    • 2020
  • This research aims to develop a deep learning-based method that automatically detects and segments the defects on railroad surfaces to reduce the cost of visual inspection of the railroad. We developed our segmentation model by modifying a fully convolutional network model [1], a well-known segmentation model used for machine learning, to detect and segment railroad surface defects. The data used in this research are images of the railroad surface with one or more defect regions. Railroad images were cropped to a suitable size, considering the long height and relatively narrow width of the images. They were also normalized based on the variance and mean of the data images. Using these images, the suggested model was trained to segment the defect regions. The proposed method showed promising results in the segmentation of defects. We consider that the proposed method can facilitate decision-making about railroad maintenance, and potentially be applied for other analyses.

Trends in Low-Power On-Device Vision SW Framework Technology (저전력 온디바이스 비전 SW 프레임워크 기술 동향)

  • Lee, M.S.;Bae, S.Y.;Kim, J.S.;Seok, J.S.
    • Electronics and Telecommunications Trends
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    • v.36 no.2
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    • pp.56-64
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    • 2021
  • Many computer vision algorithms are computationally expensive and require a lot of computing resources. Recently, owing to machine learning technology and high-performance embedded systems, vision processing applications, such as object detection, face recognition, and visual inspection, are widely used. However, on-devices need to use their resources to handle powerful vision works with low power consumption in heterogeneous environments. Consequently, global manufacturers are trying to lock many developers into their ecosystem, providing integrated low-power chips and dedicated vision libraries. Khronos Group-an international standard organization-has released the OpenVX standard for high-performance/low-power vision processing in heterogeneous on-device systems. This paper describes vision libraries for the embedded systems and presents the OpenVX standard along with related trends for on-device vision system.

Current Wheat Quality Criteria and Inspection Systems of Major Wheat Producing Countries (밀 품질평가 현황과 검사제도)

  • 이춘기;남중현;강문석;구본철;김재철;박광근;박문웅;김용호
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.47
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    • pp.63-94
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    • 2002
  • On the purpose to suggest an advanced scheme in assessing the domestic wheat quality, this paper reviewed the inspection systems of wheat in major wheat producing countries as well as the quality criteria which are being used in wheat grading and classification. Most wheat producing countries are adopting both classifications of class and grade to provide an objective evaluation and an official certification to their wheat. There are two main purposes in the wheat classification. The first objectives of classification is to match the wheat with market requirements to maximize market opportunities and returns to growers. The second is to ensure that payments to glowers aye made on the basis of the quality and condition of the grain delivered. Wheat classes has been assigned based on the combination of cultivation area, seed-coat color, kernel and varietal characteristics that are distinctive. Most reputable wheat marketers also employ a similar approach, whereby varieties of a particular type are grouped together, designed by seed coat colour, grain hardness, physical dough properties, and sometimes more precise specification such as starch quality, all of which are genetically inherited characteristics. This classification in simplistic terms is the categorization of a wheat variety into a commercial type or style of wheat that is recognizable for its end use capabilities. All varieties registered in a class are required to have a similar end-use performance that the shipment be consistent in processing quality, cargo to cargo and year to year, Grain inspectors have historically determined wheat classes according to visual kernel characteristics associated with traditional wheat varieties. As well, any new wheat variety must not conflict with the visual distinguishability rule that is used to separate wheats of different classes. Some varieties may possess characteristics of two or more classes. Therefore, knowledge of distinct varietal characteristics is necessary in making class determinations. The grading system sets maximum tolerance levels for a range of characteristics that ensure functionality and freedom from deleterious factors. Tests for the grading of wheat include such factors as plumpness, soundness, cleanliness, purity of type and general condition. Plumpness is measured by test weight. Soundness is indicated by the absence or presence of musty, sour or commercially objectionable foreign odors and by the percentage of damaged kernels that ave present in the wheat. Cleanliness is measured by determining the presence of foreign material after dockage has been removed. Purity of class is measured by classification of wheats in the test sample and by limitation for admixtures of different classes of wheat. Moisture does not influence the numerical grade. However, it is determined on all shipments and reported on the official certificate. U.S. wheat is divided into eight classes based on color, kernel Hardness and varietal characteristics. The classes are Durum, Hard Red Spring, Hard Red Winter, Soft Red Winter, Hard White, soft White, Unclassed and Mixed. Among them, Hard Red Spring wheat, Durum wheat, and Soft White wheat are further divided into three subclasses, respectively. Each class or subclass is divided into five U.S. numerical grades and U.S. Sample grade. Special grades are provided to emphasize special qualities or conditions affecting the value of wheat and are added to and made a part of the grade designation. Canadian wheat is also divided into fourteen classes based on cultivation area, color, kernel hardness and varietal characteristics. The classes have 2-5 numerical grades, a feed grade and sample grades depending on class and grading tolerance. The Canadian grading system is based mainly on visual evaluation, and it works based on the kernel visual distinguishability concept. The Australian wheat is classified based on geographical and quality differentiation. The wheat grown in Australia is predominantly white grained. There are commonly up to 20 different segregations of wheat in a given season. Each variety grown is assigned a category and a growing areas. The state governments in Australia, in cooperation with the Australian Wheat Board(AWB), issue receival standards and dockage schedules annually that list grade specifications and tolerances for Australian wheat. AWB is managing "Golden Rewards" which is designed to provide pricing accuracy and market signals for Australia's grain growers. Continuous payment scales for protein content from 6 to 16% and screenings levels from 0 to 10% based on varietal classification are presented by the Golden Rewards, and the active payment scales and prices can change with market movements.movements.

Progressive Damage Analysis of Plain Weave Fabric CFRP Orthogonal Grid Shell Under Bending Load (굽힘 하중을 받는 평직물 CFRP 직교 격자 쉘의 점진적 손상 해석)

  • Lim, Sung June;Baek, Sang Min;Kim, Min Sung;Park, Min Young;Park, Chan Yik
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.47 no.4
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    • pp.256-265
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    • 2019
  • In this paper, the progressive damage of an orthogonal grid shell fabricated with plain weave fabric CFRP under bending load was investigated. The orthogonal grids were cured with the bottom composite shell. Progressive damage analysis of an orthogonal grid shell under bending was performed using nonlinear finite element method with Hashin-Rotem failure criterion and Matzenmiller-Lubliner-Taylor(MLT) model. In addition, the three - point bending test for the structure was carried out and the test results were compared with the analysis results. The comparison results of the strain and displacement agreed well. The damage area estimated by the progressive damage analysis were compared with the visual inspection and ultrasonic non-destructive inspection.

Development of Diagnosis Application for Rail Surface Damage using Image Analysis Techniques (이미지 분석기법을 이용한 레일표면손상 진단애플리케이션 개발)

  • Jung-Youl Choi;Dae-Hui Ahn;Tae-Jun Kim
    • The Journal of the Convergence on Culture Technology
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    • v.10 no.2
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    • pp.511-516
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    • 2024
  • The recently enacted detailed guidelines on the performance evaluation of track facilities presented the necessary requirements regarding the evaluation procedures and implementation methods of track performance evaluation. However, the grade of rail surface damage is determined by external inspection (visual inspection), and there is no choice but to rely only on qualitative evaluation based on the subjective judgment of the inspector. Therefore, in this study, we attempted to develop a diagnostic application that can diagnose rail internal defects using rail surface damage. In the field investigation, rail surface damage was investigated and patterns were analyzed. Additionally, in the indoor test, SEM testing was used to construct image data of rail internal damage, and crack length, depth, and angle were quantified. In this study, a deep learning model (Fast R-CNN) using image data constructed from field surveys and indoor tests was applied to the application. A rail surface damage diagnosis application (App) using a deep learning model that can be used on smart devices was developed. We developed a smart diagnosis system for rail surface damage that can be used in future track diagnosis and performance evaluation work.

Multiple Camera Based Imaging System with Wide-view and High Resolution and Real-time Image Registration Algorithm (다중 카메라 기반 대영역 고해상도 영상획득 시스템과 실시간 영상 정합 알고리즘)

  • Lee, Seung-Hyun;Kim, Min-Young
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.49 no.4
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    • pp.10-16
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    • 2012
  • For high speed visual inspection in semiconductor industries, it is essential to acquire two-dimensional images on regions of interests with a large field of view (FOV) and a high resolution simultaneously. In this paper, an imaging system is newly proposed to achieve high quality image in terms of precision and FOV, which is composed of single lens, a beam splitter, two camera sensors, and stereo image grabbing board. For simultaneously acquired object images from two camera sensors, Zhang's camera calibration method is applied to calibrate each camera first of all. Secondly, to find a mathematical mapping function between two images acquired from different view cameras, the matching matrix from multiview camera geometry is calculated based on their image homography. Through the image homography, two images are finally registered to secure a large inspection FOV. Here the inspection system of using multiple images from multiple cameras need very fast processing unit for real-time image matching. For this purpose, parallel processing hardware and software are utilized, such as Compute Unified Device Architecture (CUDA). As a result, we can obtain a matched image from two separated images in real-time. Finally, the acquired homography is evaluated in term of accuracy through a series of experiments, and the obtained results shows the effectiveness of the proposed system and method.