Unsupervised-Learning-Based Feature-Level Fusion Method for Mura Defect Recognition

Mei, Shuang; Yang, Hua; Yin, Zhouping

doi:10.1109/tsm.2017.2648856

Cited by 48 publications

(22 citation statements)

References 14 publications

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“…After preprocessing, patches will be extracted such that the textural images can be inspected. In the testing phase, this process should be strictly conducted row by row or column by column to maximize convenience when generating the residual map [ 34 ] (in the training phase, patches can be extracted randomly). Suppose the patch size is

and the stride interval is s .…”

Section: Proposed Methodsmentioning

confidence: 99%

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Mei

Wang

Wen

2018

Sensors

Self Cite

235

121

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Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.

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and the stride interval is s .…”

Section: Proposed Methodsmentioning

confidence: 99%

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Mei

Wang

Wen

2018

Sensors

Self Cite

235

121

View full text Add to dashboard Cite

show abstract

“…Therefore, the complex manufacturing processes of these displays make the TFT-LCD subject to many kinds of defects. Many factors contribute to these defects such as the nonuniform color of color filter substrate, the anisotropy of polarizer, the non-uniformly distributed liquid crystal material, the open or shorted scanning lines, the defective TFTs, the unevenness of TFT-array substrate, and the foreign particles within liquid crystal [215]. According to [216], [217], FPD defects can be approximately classified into three types: area defects, line defects and point defects.…”

Section: Othermentioning

confidence: 99%

“…Mura (derived from Japanese which means blemish) defect is one of the achromatic defects and is widely investigated among researchers using AOI techniques. Mura defect is a local lightness variation on a surface without clear contours and causes an unpleasant sensation to the human vision [215]. Figure 12 shows different types of Mura defects investigated in literature.…”

Section: Othermentioning

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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“…(5) Defect Analysis As shown in Figure 5 , the defect analysis procedure is a process for feature extraction of the segmented defects. Feature extraction is a classical topic in feature engineering that many researchers have devoted to this field [ 15 , 16 , 17 ]. In this procedure for defects in optical fiber connector end faces, we mainly use handcrafted descriptors for feature representation, e.g., the geometric dimensions, areas, entropy, grayscale moments, and anisotropy.…”

Section: Automatic Quality Assessment For Optical Fiber End Facesmentioning

confidence: 99%

Automated Inspection of Defects in Optical Fiber Connector End Face Using Novel Morphology Approaches

Mei

Wang

Wen

et al. 2018

Sensors

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Increasing deployment of optical fiber networks and the need for reliable high bandwidth make the task of inspecting optical fiber connector end faces a crucial process that must not be neglected. Traditional end face inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. More seriously, the inspection results cannot be quantified for subsequent analysis. Aiming at the characteristics of typical defects in the inspection process for optical fiber end faces, we propose a novel method, “difference of min-max ranking filtering” (DO2MR), for detection of region-based defects, e.g., dirt, oil, contamination, pits, and chips, and a special model, a “linear enhancement inspector” (LEI), for the detection of scratches. The DO2MR is a morphology method that intends to determine whether a pixel belongs to a defective region by comparing the difference of gray values of pixels in the neighborhood around the pixel. The LEI is also a morphology method that is designed to search for scratches at different orientations with a special linear detector. These two approaches can be easily integrated into optical inspection equipment for automatic quality verification. As far as we know, this is the first time that complete defect detection methods for optical fiber end faces are available in the literature. Experimental results demonstrate that the proposed DO2MR and LEI models yield good comprehensive performance with high precision and accepted recall rates, and the image-level detection accuracies reach 96.0 and 89.3%, respectively.

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Unsupervised-Learning-Based Feature-Level Fusion Method for Mura Defect Recognition

Cited by 48 publications

References 14 publications

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

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

Automated Inspection of Defects in Optical Fiber Connector End Face Using Novel Morphology Approaches

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