An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces

Mei, Shuang; Yang, Hua; Yin, Zhouping

doi:10.1109/tim.2018.2795178

Cited by 237 publications

(106 citation statements)

References 41 publications

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“…Moreover, AE-based algorithms also demonstrate strong competitiveness in steel surface defect detection, which are reported to be fairly noise-robust. Mei et al [114] utilized convolutional denoising AE network to reconstruct image patches, combined with the reconstruction residual maps, this scheme can reliably learning final detection results, where no manual intervention is needed throughout all the detection process. Youkachen et al [18] inventively applied convolutional auto-encoder (CAE) to reconstructed the defective test images, then the reconstructed images were used to highlight the shape feature by simple post-processing algorithms, providing another good application case on miscellaneous defect detection through unsupervised learning.…”

Section: ) Unsupervised Learningmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

308

111

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Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This paper attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs, hot-and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: Statistical, spectral, model-based and machine learning. These literatures are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

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Section: ) Unsupervised Learningmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

308

111

View full text Add to dashboard Cite

show abstract

“…Based on their processing mechanics, these classifiers can be classified in two groups: (1) supervised; and (2) non-supervised or semi-supervised classifiers (see Table 3). [12,13,21,30,31,38,142,148,149,171,[192][193][194][195] Unsupervised/semi-Statistical/Novelty detection [58,65,86,[89][90][91][92]103,115,129,[196][197][198][199][200][201][202] supervised classifiers Gaussian mixture model [80,[203][204][205] Supervised classification methods incorporate the human model-as discussed in Section 3-where the application is searching for features of a predefined class. Detectable features are predefined and the classifier has to be previously trained to recognize them under supervision [40,65,90,103,142,[161][162][163].…”

Section: Supervised and Non-supervised Classifiersmentioning

confidence: 99%

“…Susan and Sharma [207] proposed a new unsupervised automated texture defect detection method that uses a Gaussian mixture entropy model to determine the optimal window size for feature extraction. Recently, Mei et al [205] developed an unsupervised learning based method by using only defect free samples for model training. The approach was carried out by reconstructing image patches with convolutional denoising autoencoder networks at different Gaussian pyramid levels, and synthesizing detection results from these different resolution channels.…”

Section: Supervised and Non-supervised Classifiersmentioning

confidence: 99%

Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Czimmermann

Ciuti

Milazzo

et al. 2020

Sensors

259

100

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This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

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“…Table 7 summarizes the defects investigated in literature using AOI system for different FPD types. [250] Mura defects [251]- [256] TFT-LCD panel defects [218], [257], [258] TFT-LCD panel micro-defects such as pinholes, scratches, particles and fingerprints [29], [223], [259], [260] Polarising film defects [261]- [263] Glass substates defects in TFT-LCD [264] Defects during photolithography process [231], [232], [265]- [267] Backlight defects [212], [213], [268], [269] GE operation defects during TFT array process [270], [271] SD operation defects during TFT array process [272], [273] Color filter defects [214] TFT array defects such as fibre defect, particle defect, pattern damage, pattern residual and pattern scratch [274]- [277] Anisotropic Conductive Film defects [278] Optical thin film defects [279] TFT-LCD pad area defects [280] LCD surface deformation for smartphones [224], [225], [225]- [227] Polarizer transparent microdefect [230] Liquid resin defects [217] Subpixel (dots) functional defects OLED [235], [236] Directional textured surface defects in OLED and PLED …”

Section: Othermentioning

confidence: 99%

“…Moreover, deep learning-based methods can require considerable computational resources and time to perform training and inferencing [244]. Mei et al in [250] proposed an algorithm to detect texture surface defects in general such as LCD panels, ceramic tiles, and textiles. Where Mura defects is one of the LCD panel defects investigated in this paper.…”

Section: Deep Learningmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, microelectronics , and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces

Cited by 237 publications

References 41 publications

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

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