A novel imaging-enhancement-based inspection method for transparent aesthetic defects in a polymeric polarizer

Deng, Yuanlong; Xu, Shao-peng; Lai, Wen-wei

doi:10.1016/j.polymertesting.2017.05.029

Cited by 14 publications

(21 citation statements)

References 16 publications

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“…The gray level of the convex or concave defects has a consistent relationship with the image distance [15]. 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.…”

Section: Defect Optical Model and Saturated Imaging Systemmentioning

confidence: 64%

“…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: 64%

Section: Introductionmentioning

confidence: 99%

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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“…Simple imaging theory is illustrated in Figure 1 . Figure 1 a presents the imaging simulation system and the optic model of defect, which are similar to those used in our previous research [ 8 , 10 ]. The simulation system, which used the TracePro software, contained a structured light source, a polymeric polarizer with a point defect, and a screen.…”

Section: Optic Simulationmentioning

confidence: 99%

“…We have previously implemented the inspection of the inner transparent aesthetic defects of a polymeric polarizer with black and white stripes; these special defects were barely observable when the polarizer was illuminated by a conventional white backlight. The stripes introduced the equivalent of a one-dimensional diaphragm, thereby considerably enhancing the imaging effect of the transparent defect [ 8 , 9 ]. When the depth of the defect and the refractive index difference (between the defect and polarizer) further decreased, the extremely slight transparent aesthetic defects (ESTADs) could not be imaged, even under structured light illumination.…”

Section: Introductionmentioning

confidence: 99%

Saturated Imaging for Inspecting Transparent Aesthetic Defects in a Polymeric Polarizer with Black and White Stripes

Chen

Zhong

et al. 2018

Materials

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Machine vision systems have been widely used in industrial production lines because of their automation and contactless inspection mode. In polymeric polarizers, extremely slight transparent aesthetic defects are difficult to detect and characterize through conventional illumination. To inspect such defects rapidly and accurately, a saturated imaging technique was proposed, which innovatively uses the characteristics of saturated light in imaging by adjusting the light intensity, exposure time, and camera gain. An optical model of defect was established to explain the theory by simulation. Based on the optimum experimental conditions, active two-step scanning was conducted to demonstrate the feasibility of this detection scheme, and the proposed method was found to be efficient for real-time and in situ inspection of defects in polymer films and products.

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“…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. Deng et al proposed an automated inspection method for detecting slight aesthetic defects in polymeric polarizers.…”

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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A novel imaging-enhancement-based inspection method for transparent aesthetic defects in a polymeric polarizer

Cited by 14 publications

References 16 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

Saturated Imaging for Inspecting Transparent Aesthetic Defects in a Polymeric Polarizer with Black and White Stripes

Lightweight efficient network for defect classification of polarizers

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