• Title/Summary/Keyword: Defect detection

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Automatic Metallic Surface Defect Detection using ShuffleDefectNet

  • Anvar, Avlokulov;Cho, Young Im
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.3
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    • pp.19-26
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    • 2020
  • Steel production requires high-quality surfaces with minimal defects. Therefore, the detection algorithms for the surface defects of steel strip should have good generalization performance. To meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. In this paper, we proposed a ShuffleDefectNet defect detection system based on deep learning. The proposed defect detection system exceeds state-of-the-art performance for defect detection on the Northeastern University (NEU) dataset obtaining a mean average accuracy of 99.75%. We train the best performing detection with different amounts of training data and observe the performance of detection. We notice that accuracy and speed improve significantly when use the overall architecture of ShuffleDefectNet.

Implementation of Paper Cutting Defect Detection System Based on Local Binary Pattern Analysis (국부 이진 패턴 분석에 기초한 지절 결함 검출 시스템 구현)

  • Kim, Jin-Soo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.17 no.9
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    • pp.2145-2152
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    • 2013
  • Paper manufacturing industries have huge facilities with automatic equipments. Especially, in order to improve the efficiency of the paper manufacturing processes, it is necessary to detect the paper cutting defect effectively and to classify the causes correctly. In this paper, we review the problems of web monitoring system and web inspection system that have been traditionally used in industries for defect detection. Then we propose a novel paper cutting defect detection method based on the local binary pattern analysis and its implementation to mitigate the practical problems in industry environment. The proposed algorithm classifies the defects into edge-type and region-type and then it is shown that the proposed system works stably on the real paper cutting defect detection system.

An Improved Defect Detection Algorithm of Jean Fabric Based on Optimized Gabor Filter

  • Ma, Shuangbao;Liu, Wen;You, Changli;Jia, Shulin;Wu, Yurong
    • Journal of Information Processing Systems
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    • v.16 no.5
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    • pp.1008-1014
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    • 2020
  • Aiming at the defect detection quality of denim fabric, this paper designs an improved algorithm based on the optimized Gabor filter. Firstly, we propose an improved defect detection algorithm of jean fabric based on the maximum two-dimensional image entropy and the loss evaluation function. Secondly, 24 Gabor filter banks with 4 scales and 6 directions are created and the optimal filter is selected from the filter banks by the one-dimensional image entropy algorithm and the two-dimensional image entropy algorithm respectively. Thirdly, these two optimized Gabor filters are compared to realize the common defect detection of denim fabric, such as normal texture, miss of weft, hole and oil stain. The results show that the improved algorithm has better detection effect on common defects of denim fabrics and the average detection rate is more than 91.25%.

Fast Defect Detection of PCB using Ultrasound Thermography (초음파 서모그라피를 이용한 빠른 PCB 결함 검출)

  • Cho, Jai-Wan;Jung, Hyun-Kyu;Seo, Yong-Chil;Jung, Seung-Ho;Kim, Seung-Ho
    • Proceedings of the KIEE Conference
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    • pp.273-275
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    • 2005
  • Active thermography is being used since several years for remote non-destructive testing. It provides thermal images for remote detection and imaging of damages. Also, it is based on propagation and reflection of thermal waves which are launched from the surface into the inspected component by absorption of modulated radiation. For energy deposition, it use external heat sources (e.g., halogen lamp or convective heating) or internal heat generation (e.g., microwaves, eddy current, or elastic wave). Among the external heat sources, the ultrasound is generally used for energy deposition because of defect selective heating up. The heat source generating a thermal wave is provided by the defect itself due to the attenuation of amplitude modulated ultrasound. A defect causes locally enhanced losses and consequently selective heating up. Therefore amplitude modulation of the injected ultrasonic wave turns a defect into a thermal wave transmitter whose signal is detected at the surface by thermal infrared camera. This way ultrasound thermography(UT) allows for selective defect detection which enhances the probability of defect detection in the presence of complicated intact structures. In this paper the applicability of UT for fast defect detection is described. Examples are presented showing the detection of defects in PCB material. Measurements were performed on various kinds of typical defects in PCB materials (both Cu metal and non-metal epoxy). The obtained thermal image reveals area of defect in row of thick epoxy material and PCB.

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Local Binary Feature and Adaptive Neuro-Fuzzy based Defect Detection in Solar Wafer Surface (지역적 이진 특징과 적응 뉴로-퍼지 기반의 솔라 웨이퍼 표면 불량 검출)

  • Ko, JinSeok;Rheem, JaeYeol
    • Journal of the Semiconductor & Display Technology
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    • v.12 no.2
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    • pp.57-61
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    • 2013
  • This paper presents adaptive neuro-fuzzy inference based defect detection method for various defect types, such as micro-crack, fingerprint and contamination, in heterogeneously textured surface of polycrystalline solar wafers. Polycrystalline solar wafer consists of various crystals so the surface of solar wafer shows heterogeneously textures. Because of this property the visual inspection of defects is very difficult. In the proposed method, we use local binary feature and fuzzy reasoning for defect detection. Experimental results show that our proposed method achieves a detection rate of 80%~100%, a missing rate of 0%~20% and an over detection (overkill) rate of 9%~21%.

A Micro-defect Detection of Cold Rolled Steel (냉연 강판의 미세 결함 검출 기술)

  • Yun, Jong Pil
    • Journal of Institute of Control, Robotics and Systems
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    • v.22 no.4
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    • pp.247-252
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    • 2016
  • In this paper, we propose a new defect detection technology for micro-defect on the surface of steel products. Due to depth and size of microscopic defect, slop of surface and vibration of strip, the conventional optical method cannot guarantee the detection performance. To solve the above-mentioned problems and increase signal to noise ratio, a novel retro-schlieren method that consists of retro reflector and knife edge is proposed. Moreover dual switching lighting method is also applied to distinguish uneven micro defects and surface noise. In proposed method, defective regions are represented by a black and white pattern. This pattern is detected by a defect detection algorithm with Gabor filter. Experimental results by simulator for sample defects of cold rolled steel show that the proposed method is effective.

Automatic Defect Detection from SEM Images of Wafers using Component Tree

  • Kim, Sunghyon;Oh, Il-seok
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.17 no.1
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    • pp.86-93
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    • 2017
  • In this paper, we propose a novel defect detection method using component tree representations of scanning electron microscopy (SEM) images. The component tree contains rich information about the topological structure of images such as the stiffness of intensity changes, area, and volume of the lobes. This information can be used effectively in detecting suspicious defect areas. A quasi-linear algorithm is available for constructing the component tree and computing these attributes. In this paper, we modify the original component tree algorithm to be suitable for our defect detection application. First, we exclude pixels that are near the ground level during the initial stage of component tree construction. Next, we detect significant lobes based on multiple attributes and edge information. Our experiments performed with actual SEM wafer images show promising results. For a $1000{\times}1000$ image, the proposed algorithm performed the whole process in 1.36 seconds.

Improvement of Defect Detection in TFT-Array Panel

  • Chung, Kyo-Young
    • 한국정보디스플레이학회:학술대회논문집
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    • pp.594-597
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    • 2005
  • This paper shows that the defect detection in TFTarray panel can be improved by using newly developed software solution without adding additional hardware instruments. Some issues are reviewed in current TFT array test and new algorithm is explained for detecting more real defects without paying the penalty of reporting more false defects in TFT array test.

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TFT-LCD Defect Blob Detection based on Sequential Defect Detection Method (순차적 결함 검출 방법에 기반한 TFT-LCD 결함 영역 검출)

  • Lee, Eunyoung;Park, Kil-Houm
    • Journal of the Korea Industrial Information Systems Research
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    • v.20 no.2
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    • pp.73-83
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    • 2015
  • This paper proposes a TFT-LCD defect blob detection algorithm using the sequential defect detection method. First, for every pixel, a defect possibility is determined by the intensity difference and the defect candidates are detected according to the sequential defect detection method. For detected candidate pixels, the defect probability that indicates a potential included in the defect according to the each step. By applying the morphological operation, blobs are comprised of the detected candidates and the defect blobs are detected using the defect possibility of blobs. The validity of the proposed method was demonstrated a simulated image and also then it was tested a real TFT-LCD image. By the experimental results, the proposed method is very effective in TFT-LCD detect detection.