• Title/Summary/Keyword: Defect detection

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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.

A Comparative Study on Deep Learning Models for Scaffold Defect Detection (인공지지체 불량 검출을 위한 딥러닝 모델 성능 비교에 관한 연구)

  • Lee, Song-Yeon;Huh, Yong Jeong
    • Journal of the Semiconductor & Display Technology
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    • v.20 no.2
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    • pp.109-114
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    • 2021
  • When we inspect scaffold defect using sight, inspecting performance is decrease and inspecting time is increase. We need for automatically scaffold defect detection method to increase detection accuracy and reduce detection times. In this paper. We produced scaffold defect classification models using densenet, alexnet, vggnet algorithms based on CNN. We photographed scaffold using multi dimension camera. We learned scaffold defect classification model using photographed scaffold images. We evaluated the scaffold defect classification accuracy of each models. As result of evaluation, the defect classification performance using densenet algorithm was at 99.1%. The defect classification performance using VGGnet algorithm was at 98.3%. The defect classification performance using Alexnet algorithm was at 96.8%. We were able to quantitatively compare defect classification performance of three type algorithms based on CNN.

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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.

Performance evaluation of wavelet and curvelet transforms based-damage detection of defect types in plate structures

  • Hajizadeh, Ali R.;Salajegheh, Javad;Salajegheh, Eysa
    • Structural Engineering and Mechanics
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    • v.60 no.4
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    • pp.667-691
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    • 2016
  • This study focuses on the damage detection of defect types in plate structures based on wavelet transform (WT) and curvelet transform (CT). In particular, for damage detection of structures these transforms have been developed since the last few years. In recent years, the CT approach has been also introduced in an attempt to overcome inherent limitations of traditional multi-scale representations such as wavelets. In this study, the performance of CT is compared with WT in order to demonstrate the capability of WT and CT in detection of defect types in plate structures. To achieve this purpose, the damage detection of defect types through defect shape in rectangular plate is investigated. By using the first mode shape of plate structure and the distribution of the coefficients of the transforms, the damage existence, the defect location and the approximate shape of defect are detected. Moreover, the accuracy and performance generality of the transforms are verified through using experimental modal data of a plate.

Defect Detection algorithm of TFT-LCD Polarizing Film using the Probability Density Function based on Cluster Characteristic (TFT-LCD 영상에서 결함 군집도 특성 기반의 확률밀도함수를 이용한 결함 검출 알고리즘)

  • Gu, Eunhye;Park, Kil-Houm
    • Journal of Korea Multimedia Society
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    • v.19 no.3
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    • pp.633-641
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    • 2016
  • Automatic defect inspection system is composed of the step in the pre-processing, defect candidate detection, and classification. Polarizing films containing various defects should be minimized over-detection for classifying defect blobs. In this paper, we propose a defect detection algorithm using a skewness of histogram for minimizing over-detection. In order to detect up defects with similar to background pixel, we are used the characteristics of the local region. And the real defect pixels are distinguished from the noise using the probability density function. Experimental results demonstrated the minimized over-detection by utilizing the artificial images and real polarizing film images.

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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TFT-LCD Defect Detection based on Histogram Distribution Modeling (히스토그램 분포 모델링 기반 TFT-LCD 결함 검출)

  • Gu, Eunhye;Park, Kil-Houm;Lee, Jong-Hak;Ryu, Gang-Soo;Kim, Jungjoon
    • Journal of Korea Multimedia Society
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    • v.18 no.12
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    • pp.1519-1527
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    • 2015
  • TFT-LCD automatic defect inspection system for detecting defects in place of the visual tester does pre-processing, candidate defect pixel detection, and recognition and classification through a blob analysis. An over-detection result of defects acts as an undue burden of blob analysis for recognition and classification. In this paper, we propose defect detection method based on the histogram distribution modeling of TFT-LCD image to minimize over-detection of candidate defective pixels. Primary defect candidate pixels are detected estimating the skewness of the luminance distribution histogram of the background pixels. Based on the detected defect pixels, the defective pixels other than noise pixels are detected using the distribution histogram model of the local area. Experimental results confirm that the proposed method shows an excellent defect detection result on the image containing the various types of defects and the reduction of the degree of over-detection as well.

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%.

Sequential Defect Region Segmentation according to Defect Possibility in TFT-LCD Image (TFT-LCD영상에서 결함 가능성에 따른 순차적 결함영역 분할)

  • Chang, Chung Hwan;Lee, SeungMin;Park, Kil-Houm
    • Journal of Korea Multimedia Society
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    • v.23 no.5
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    • pp.633-640
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    • 2020
  • Defect region segmentation of TFT-LCD images is performed by combining defect pixels detected by a defect detection method into defect region, or by using morphological operations to segment defect region. Therefore, the result of segmentation of the defect region is highly dependent on the defect detection result. In this paper, we propose a method which segments defect regions sequentially according to the possibility of being included in defect regions in TFT-LCD images. The proposed method repeats the process of detecting a seed using the median value and the median absolute deviation of the image, and segments the defect region using the seeded region growing method. We confirmed the superiority of the proposed method to segment defect regions using pseudo-images and real TFT-LCD images.

Nondestructive Detection of Defect in a Pipe Using Thermography

  • Choi, Hee-Seok;Joung, Ok-Jin;Kim, Young-Han
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1413-1416
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    • 2005
  • An infrared temperature sensor module developed for the detection of defects in a plate was modified to use in a cylinder. A set of optical fiber leads and a mechanism maintaining sensor-object distance constant were utilized for the modification of the IR sensor module. The detection performance was experimentally investigated, and the measured temperature was also compared with computed temperature distribution. The experimental outcome indicates that the detection of a simulated defect is readily available. The temperature distribution is better for defect detection than that with the previous device. In addition, the measured distribution is comparable to the calculated one using a heat conduction equation. The developed device of defect detection is suitable to be utilized in chemical processes where most of vessels and piping systems are in the shape of a cylinder.

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