• 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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.141-147
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• 1996

In this paper an improved morphological algorithm for directional defect detection is proposed, where the defect is parallel to the texture image. The algorithm is based on obtaining the background image while removing the defect by comparing every directional morphological result with max or min except that of defect. The defect can of defect and the background image. For a computer simulation, it is shown that the proposed method has better performance than the conventional algorithm.

• 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.

• 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 Information Processing Systems
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• v.19 no.1
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• pp.109-117
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• 2023

Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1-3 percentage points.

• Journal of Korea Society of Industrial Information Systems
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.245-265
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• 2022

In response to problems such as insufficient extraction information, low detection accuracy, and frequent misdetection in the field of Thangka image defects, this paper proposes a YOLOv5 prediction algorithm fused with the attention mechanism. Firstly, the Backbone network is used for feature extraction, and the attention mechanism is fused to represent different features, so that the network can fully extract the texture and semantic features of the defect area. The extracted features are then weighted and fused, so as to reduce the loss of information. Next, the weighted fused features are transferred to the Neck network, the semantic features and texture features of different layers are fused by FPN, and the defect target is located more accurately by PAN. In the detection network, the CIOU loss function is used to replace the GIOU loss function to locate the image defect area quickly and accurately, generate the bounding box, and predict the defect category. The results show that compared with the original network, YOLOv5-SE and YOLOv5-CBAM achieve an improvement of 8.95% and 12.87% in detection accuracy respectively. The improved networks can identify the location and category of defects more accurately, and greatly improve the accuracy of defect detection of Thangka images.

• 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%.

• 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 Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.337-343
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• 1996

In this paper, we investigate the defect detection by making use of pre-made reference image data and classify the defects by using the artificial neural network. The approach is composed of three main parts. The first step consists of a proper generation of two reference image data by using a low level morphological technique. The second step proceeds by performing three times logical bit operations between two ready-made reference images and just captured image to be tested. This results in defects image only. In the third step, by extracting four features from each detected defect, followed by assigning them into the input nodes of an already trained artificial neural network we can obtain a defect class corresponding to the features. All of the image data are formed in a bit level for the reduction of data size as well as time saving. Experimental results show that proposed algorithms are found to be effective for flexible defect detection, robust classification, and high speed process by adopting a simple logic operation.