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Deep Learning based MURA Defect Detection
2019
EAI Endorsed Transactions on Cloud Systems
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We also conclude that state-of-the-art network for general object detection can be reused with the help of Transfer Learning (TL) concept and fine-tuned with MURA domain specific optimizations mentioned ...
Manual detection is subjective, error prone, very tedious and time consuming. Even when the type of MURA defects can be ascertained manually, the exact bounding box for defect is hard to determine. ...
Deep Learning based MURA Defect Detection EAI Endorsed Transactions on
Cloud Systems
03 2019 -07 2019 | Volume 5 | Issue 15 | e6
4
Ramya Bagavath Singh et al. ...
doi:10.4108/eai.16-7-2019.162217
fatcat:iq2kbwjotfa63juvmv3muhjjke
Survey of Mura Defect Detection in Liquid Crystal Displays Based on Machine Vision
2021
Crystals
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Finally, the future development trend and research direction of Mura defect detection based on machine vision can be drawn by this study. ...
In the development of LCD technology, the detection of Mura defects is a key concern in the manufacturing process. ...
For the classifiers based on deep learning, Zeng et al. [64] adopted the BP neural network to extract and detect Mura defects in experiments, and identified 200 suspected Mura defect images. ...
doi:10.3390/cryst11121444
fatcat:f64khi64lvbqbndnza6maiatmy
A Mura Detection Model Based on Unsupervised Adversarial Learning
2021
IEEE Access
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(a) (b) (c) (d) In the field of Mura detection, supervised deep learning has been carried out. Heeyeon Jo et al. [28] used an automatic encoder to separate defects. Hua Yang et al. ...
Anomaly detection is common in various fields [1] [2] [3] [4] [5] [6] . In [7, 8] , Young-Jin Cha et al. successfully applied deep learning to defect detection and achieved good detection results. ...
doi:10.1109/access.2021.3069466
fatcat:m6wo6fvbnfd6dn3z7mr5tyl3dq
A Mura Detection Method Based on an Improved Generative Adversarial Network
2021
IEEE Access
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INDEX TERMS Mura detection, multiple supervision, UADD generator. and her main research interests include deep learning and defect detection algorithm. ...
CHUNXU CHEN received the M.S. degree in South and his main research interests include deep learning and defect detection algorithm. ...
Researches [22, 23] both provide a transfer learning based method that requires less training samples to detect Mura. ...
doi:10.1109/access.2021.3076792
fatcat:rmdssuzwrngyjih47aewtxqjru
Regularized Auto-Encoder-Based Separation of Defects from Backgrounds for Inspecting Display Devices
2019
Electronics
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Because the conventional methods face problems such as imperfect reconstruction and difficulty of selecting the bases for low-rank approximation, we have studied a deep-learning-based foreground reconstruction ...
Separation of defects has important applications such as determining whether the detected defects are truly defective and the quantification of the degree of defectiveness. ...
Unlike the pixel-wise segmentation, the proposed method generates the defect image by deep-learning-based pixel regression. ...
doi:10.3390/electronics8050533
fatcat:wegdnwvkgzcfbl7fpgdva6hjsy
A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry
2020
IEEE Access
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Mei et al. in [215] proposed a method that combined handcrafted features and unsupervised deep learning-based features to detect Mura defects in TFT-LCD. ...
DEEP LEARNING Deep learning has recently become the most influential technology in machine vision and pattern recognition problems [383] . ...
According to that, the keywords were adjusted to explore more about the most commonly investigated defects. ...
doi:10.1109/access.2020.3029127
fatcat:hoimi667cndsrimsnwasamtdey
Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey
[article]
2022
arXiv
pre-print
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However, the severe imbalanced data, especially the limited number of defect samples, has been a long-standing problem that hinders the successful application of deep learning algorithms. ...
Synthetic defect data generation can help address this issue. ...
To achieve accurate defect detection for display panels, deep learning can be used [8] . ...
arXiv:2203.03429v1
fatcat:ozjhpcusv5gotle55xscrsj3m4
A CNN-based Transfer Learning Method for Defect Classification in Semiconductor Manufacturing
2019
IEEE transactions on semiconductor manufacturing
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Index Terms-Machine learning, deep learning, transfer learning, defect classification, semiconductor manufacturing. ...
To support the first and third engineer's analytical work, we use a convolutional neural network based on the transfer learning method for automatic defect classification. ...
Yang et al. proposed a transfer learning based online Mura defect classification method [19] . ...
doi:10.1109/tsm.2019.2941752
fatcat:ojre5mnppvhtbbxgi4svs7koni
DETECTION OF OSTEOPOROSIS IN DEFECTED BONES USING RADTORCH AND DEEP LEARNING TECHNIQUES
2021
International Journal of Engineering Applied Sciences and Technology
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In this paper a significant learning model reliant on a ResNet50 and XGBoost Classifier has been used for predicting gouts or osteoporosis in MURA V2 dataset by using RADTorch library in order to preprocess ...
image of X-rays to identify the defects easily. ...
It can realize new information the achievability of deep learning systems for MRI picture investigation. ...
doi:10.33564/ijeast.2021.v06i04.015
fatcat:zhw3ycwuhnhehknr3ckpv4xexy
Effective Defect Detection Method Based on Bilinear Texture Features for LGPs
2021
IEEE Access
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These defects lack salient visual attributes, such as edge-based and region-based features, and as such, traditional methods fail to detect them. ...
Automatic defect detection of light guide plates (LGPs) is an important task in the manufacture of liquid crystal displays. ...
[14] used region-based and modified region-based segmentation methods to detect Mura defects. ...
doi:10.1109/access.2021.3111410
fatcat:ox2w46xv6nc67epykpmxqt5qi4
A Novel Multicategory Defect Detection Method Based on the Convolutional Neural Network Method for TFT-LCD Panels
2022
Mathematical Problems in Engineering
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Defects on thin film transistor liquid crystal display (TFT-LCD) panel could be divided into either macro- or microdefects, depending on if they are easy to be detected by the naked eye or not. ...
The model could finish the detection and classification process automatically to replace the human inspection. ...
after edge detection. e basic process of defect classification using the deep learning model is shown in Figure 10 . ...
doi:10.1155/2022/6505372
fatcat:um6gbnmh7femnehvcx2zs5kjfq
Classification of Microscopic Laser Engraving Surface Defect Images Based on Transfer Learning Method
2021
Electronics
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Deep convolutional networks integrate feature extraction and classification into self-learning, but require large datasets. ...
In recent years, convolutional neural networks (CNNs) have achieved excellent results in image classification tasks with the development of deep learning. ...
The remaining paper is organized as follows: Section 2 introduces the application of machine learning and deep learning in defect detection. ...
doi:10.3390/electronics10161993
fatcat:d3sz3cntr5gh5pnxovcqflxj4q
Yolov4 High-Speed Train Wheelset Tread Defect Detection System Based on Multiscale Feature Fusion
2022
Journal of Advanced Transportation
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This study aims to solve this problem by proposing a Yolov4-based multiscale feature fusion detection system for high-speed train wheel tread defects. ...
Experimental results show that the proposed method is effective at detecting defects in the wheel treads of high-speed trains. ...
[33] used transfer learning to detect Mura defects in LCD panels, PCB defects, electrode defects in lithium batteries, and surface defects in metal parts. Kim et al. ...
doi:10.1155/2022/1172654
fatcat:6jjmn5g34rdctichhruj4bzu5i
Table of Contents
2021
IEEE Transactions on Reliability
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Yang 1026 DeepEutaxy: Diversity in Weight Search Direction for Fixing Deep Learning Model Training Through Batch Prioritization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
System and Software Reliability: Application or Empirical Studies -RED: Fractal Residual Based Real-Time Detection of the LDoS Attack . . . . . D. Tang, Y. Feng, S. Zhang, and Z. ...
doi:10.1109/tr.2021.3104690
fatcat:gppiuscwljdv5caek7t4ccz4gm
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products
[article]
2021
arXiv
pre-print
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However, casting defect detection is a challenging task due to diversity and variation in defects' appearance. ...
Accordingly, leveraging the transfer learning paradigm, we first apply four powerful CNN-based models (VGG16, ResNet50, DenseNet121, and InceptionResNetV2) on a small dataset to extract meaningful features ...
CONCLUSION This work proposes the deep transfer learning method as one of the most powerful paradigms in ML for the defect detection task using the casting product images. ...
arXiv:2107.11643v1
fatcat:v6oyqc2x4jcoxikp5v4fl5jevy
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