Automatic Visual Defect Detection Using Texture Prior and Low-Rank Representation

Huangpeng, Qizi; Hong, Zhang; Zeng, Xiangrong; Huang, Wenwei

doi:10.1109/access.2018.2852663

Cited by 44 publications

(30 citation statements)

References 18 publications

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“…Susan et al [96] proposed Gaussian mixture entropy model for defect detection, which is specialized in identifying miscellaneous defects such as holes and stains. Based on low-rank representation, Yan et al [97] utilized smooth-sparse decomposition (SSD) model for anomaly detection in images, Huangpeng et al [98] proposed a novel weighted low-rank reconstruction model for automatic visual defect detection, and Zhou et al [99] presented a double low-rank and sparse decomposition (DLRSD) model to obtain the defective region of steel sheet surface. These approaches are reported to perform well.…”

Section: ) Other Latest Reported Model-basedmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

339

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: ) Other Latest Reported Model-basedmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

339

111

View full text Add to dashboard Cite

show abstract

“…However, if the defected region is relatively large, this may be reflected to the background features and will affect the background reconstruction method [351]. Furthermore, this method can only solve LCD images with simple background [352]. Similar to PCA, Singular Value Decomposition (SVD) is another linear method used to extract the significant feature components of the image, which can be also used to calculate PCs.…”

Section: ) Model-based Feature Extractionmentioning

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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“…Recently, although deep learning-based approaches have been used to solve several problems in the field of civil engineering, some problems are complex, and more optimal approaches than deep learning-based approaches have sometimes been used (Amezquita-Sanchez & Adeli, 2015b;Huangpeng, Zhang, Zeng, & Huang, 2018;Liao, 2017;Poskus, Rodgers, Zhou, & Chase, 2018;Qarib and Adeli, 2015;Wang, Zhang, Wang, Braham, & Qiu, 2018). For example, Wang et al (2018) developed an automatic method for measuring crack width using binary crack map images.…”

Section: Related Work Smentioning

confidence: 99%

“…They introduced a new crack width definition and formulated it using the Laplace equation so that crack width can be continuously and unambiguously measured, and they showed that their method was effective for characterizing propagation behavior of cracks with small widths. Although cracks are one of the most important types of distress, methods for detecting other types of distress have also been studied (Huangpeng et al., ; Poskus et al., ). Huangpeng et al focused on the use of texture features to construct prior maps for distress detection.…”

Section: Related Workmentioning

confidence: 99%

Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures

Maeda

Takahashi

Ogawa

et al. 2019

Computer aided Civil Eng

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This paper presents a convolutional sparse coding (CSC)‐based deep random vector functional link network (CSDRN) for distress classification of road structures. The main contribution of this paper is the introduction of CSC into a feature extraction scheme in the distress classification. CSC can extract visual features representing characteristics of target images because it can successfully estimate optimal convolutional dictionary filters and sparse features as visual features by training from a small number of distress images. The optimal dictionaries trained from distress images have basic components of visual characteristics such as edge and line information of distress images. Furthermore, sparse feature maps estimated on the basis of the dictionaries represent both strength of the basic components and location information of regions having their components, and these maps can represent distress images. That is, sparse feature maps can extract key components from distress images that have diverse visual characteristics. Therefore, CSC‐based feature extraction is effective for training from a limited number of distress images that have diverse visual characteristics. The construction of a novel neural network, CSDRN, by the use of a combination of CSC‐based feature extraction and the DRN classifier, which can also be trained from a small dataset, is shown in this paper. Accurate distress classification is realized via the CSDRN.

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Automatic Visual Defect Detection Using Texture Prior and Low-Rank Representation

Cited by 44 publications

References 18 publications

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Automated Visual Defect Detection for Flat Steel Surface: A Survey

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

Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures

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