Aesthetic defect characterization of a polymeric polarizer via structured light illumination

Lai, Wen-wei; Zeng, Xiao-xing; Jian, He; Deng, Yuanlong

doi:10.1016/j.polymertesting.2016.05.011

Cited by 24 publications

(19 citation statements)

References 17 publications

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“…One of our previous work [13] indicates that the transparent microdefects of polarizers can be approximated as a microscale planoconvex lens model, as shown in Figure 1a. Using structured-light illumination [7], we can effectively improve the imaging contrast at the position of microdefects and increase the detection accuracy. The optical simulation software TracePro is used to model and simulate the dents and bumps of a polarizer, as shown in Figure 1b.…”

Section: Defect Optical Model and Saturated Imaging Systemmentioning

confidence: 99%

“…Their system inspects an area of 36mm × 27mm within 0.3 s. Mei et al [6] proposed an unsupervised learning-based feature-level fusion approach to detect mura defects in TFT-LCD panels. In our previous work, a structured backlight was used to enhance the contrast of imaging, thus facilitating detection of polarizers' defects [7].…”

Section: Introductionmentioning

confidence: 99%

“…In our previous studies [7,[13][14][15], we have focused on an imaging mechanism for transparent microdefects, such as dents and bumps, and proposed a so-called saturated imaging method to obtain high-contrast defect images. Through simulation and experimentation, we have found that the saturated image of a defect varies regularly with its stereo shape.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Shape Estimation of Transparent Microdefects with Manifold Learning and Regression on a Set of Saturated Images

Deng¹,

Pan²,

Zhong³

2020

Applied Sciences

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Featured Application: The paper provides a convenient method to determine if a suspected microdefect of polarizer film is real. This is useful for manufacturing of diverse flexible/ transparent thin-film. Abstract:In the industry of polymer film products such as polarizers, measuring the three-dimensional (3D) contour of the transparent microdefects, the most common defects, can crucially affect what further treatment should be taken. In this paper, we propose an efficient method for estimating the 3D shape of defects based on regression by converting the problem of direct measurement into an estimation problem using two-dimensional imaging. The basic idea involves acquiring structured-light saturated imaging data on transparent microdefects; integrating confocal microscopy measurement data to create a labeled data set, on which dimensionality reduction is performed; using support vector regression on a low-dimensional small-set space to establish the relationship between the saturated image and defects' 3D attributes; and predicting the shape of new defect samples by applying the learned relationship to their saturated images. In the discriminant subspace, the manifold of saturated images can clearly show the changing attributes of defects' 3D shape, such as depth and width. The experimental results show that the mean relative error (MRE) of the defect depth is 3.64% and the MRE of the defect width is 1.96%. The estimation time consumed in the Matlab platform is less than 0.01 s. Compared with precision measuring instruments such as confocal microscopes, our estimation method greatly improves the efficiency of quality control and meets the accuracy requirement of automated defect identification. It is therefore suitable for complete inspection of products.

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Section: Defect Optical Model and Saturated Imaging Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Shape Estimation of Transparent Microdefects with Manifold Learning and Regression on a Set of Saturated Images

Deng¹,

Pan²,

Zhong³

2020

Applied Sciences

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“…Advancing from this, projector-based approaches trying to identify subsurface miscolorings using structured light have also been suggested [3].…”

Section: Related Workmentioning

confidence: 99%

An Image-Based Method for Objectively Assessing Injection Moulded Plastic Quality

et al. 2017

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Abstract. In high volume productions based on casting processes, like high-pressure die casting (HPDC) or injection moulding, there is a wide range of variables that affect the end quality of produced parts. These variables include production parameters (temperature, pressure, mixture), and external factors (humidity, temperature, etc.). With this many variables it is a challenge to maintain a stable output quality, wherefore massive amounts of resources are spent on quality assurance (QA) of produced parts. Currently, this QA is done manually through visual inspection. We demonstrate how a multispectral imaging system can be used to automatically rate the quality of a produced part using an autocorrelation and a Fourier-based method. These methods are compared with human rankings and achieve good correlations on a variety of samples.

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“…The system applies stripe‐structured light illumination and image processing for defect detection. Lai et al designed a machine vision system that used an LCD to generate binary stripe patterns for enhancing defect imaging. Deng et al developed an automatic inspection method using the structured lighting technique to significantly enhance defect imaging.…”

Section: Introductionmentioning

confidence: 99%

Lightweight efficient network for defect classification of polarizers

Liu

Sun

Wang

et al. 2020

Concurrency and Computation

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In industrial production, detecting polarizer defects online and in real time is necessary. Existing methods of detecting polarizer defects based on deep learning can ensure the accuracy of classification; however, there are several issues associated with these methods. These include the models having low detection speed, consuming large amounts of memory, and being difficult to be transplanted into online detection systems. To solve the aforementioned problems, a lightweight efficient network (LWEN) structure based on deep learning was designed, which improves the standard convolution layer and the fully connected (FC) layer to minimize the training model size and increase the speed of classification without reducing the accuracy of classification. First, a new building block, the shunt module, was designed to build the LWEN.Subsequently, a global average pooling layer was used to reduce the spatial resolution to 1 before the FC layer. These key technologies were designed to reduce the number of network parameters and minimize the model size of the network. Experimental results show that the proposed LWEN outperforms the state-of-the-art approaches in terms of classification accuracy, speed, and model size.

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Aesthetic defect characterization of a polymeric polarizer via structured light illumination

Cited by 24 publications

References 17 publications

Efficient Shape Estimation of Transparent Microdefects with Manifold Learning and Regression on a Set of Saturated Images

Efficient Shape Estimation of Transparent Microdefects with Manifold Learning and Regression on a Set of Saturated Images

An Image-Based Method for Objectively Assessing Injection Moulded Plastic Quality

Lightweight efficient network for defect classification of polarizers

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