• 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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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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.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.386-393
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• 2010

A defect in an image is a set of pixels forming an irregular shape. Since a defect, in most cases, is not easy to be modeled mathematically, the defect detection problem still resides in a research area. If a given image, however, composed by certain patterns, a defect can be detected by the fact that a non-defect area should be explained by another patch in terms of a rotation, translation, and noise. In this paper, therefore, the defect detection method for a repeated multi-patterned image is proposed. The proposed defect detection method is composed of three steps. First step is the interest point detection step, second step is the selection step of a appropriate patch size, and the last step is the decision step. The proposed method is illustrated using SEM images of semiconductor wafer samples.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.63-68
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• 2022

In this study, we produced defect detection models using parallel convolution neural networks. If convolution neural networks are constructed parallel type, the model's detection accuracy will increase and detection time will decrease. We produced parallel-type defect detection models using 4 types of convolutional algorithms. The performance of models was evaluated using evaluation indicators. The model's performance is detection accuracy and detection time. We compared the performance of each parallel model. The detection accuracy of the model using AlexNet is 97 % and the detection time is 0.3 seconds. We confirmed that when AlexNet algorithm is constructed parallel type, the model has the highest performance.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.68-71
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• 2006

Active thermography has been used for several years in the field of 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 are 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.

• 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%.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1288-1296
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• 2016

The reliable detection of the limited defect in TFT-LCD images is difficult due to the small intensity difference with the background. However, the proposed detection method reliably detects the limited defect by enhancing the TFT-LCD image based on the cumulative histogram and then detecting the defect through the mean and standard deviation of the enhanced image. Notably, an image enhancement using a cumulative histogram increases the intensity contrast between the background and the limited defect, which then allows defects to be detected by using the mean and standard deviation of the enhanced image. Furthermore, through the comparison with the histogram equalization, we confirm that the proposed algorithm suppresses the emphasis of the noise. Experimental comparative results using real TFT-LCD images and pseudo images show that the proposed method detects the limited defect more reliably than conventional methods.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.