Defect identification method for electrowetting display screen

A defect recognition and display technology, applied in the field of deep learning and image processing, can solve problems such as being easily affected by environmental factors such as light, cumbersome manual feature extraction, and low robustness, so as to improve training speed and recognition accuracy, and accelerate The effect of network convergence and strong robustness

Inactive Publication Date: 2018-04-20
FUZHOU UNIV
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Problems solved by technology

The traditional method is cumbersome to manually extract features, is easily affected by environmental factors such as light, and has low robustness.

Method used

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  • Defect identification method for electrowetting display screen
  • Defect identification method for electrowetting display screen
  • Defect identification method for electrowetting display screen

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Embodiment Construction

[0027] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0028] A method for identifying defects in an electrowetting display screen of the present invention is realized by using a convolutional neural network added to a batch normalization algorithm; the convolutional neural network includes four convolutional layers, three pooling layers, three Layer batch normalization layer, two layers of fully connected layers, two layers of Dropout layer and output layer; by adding batch normalization layer after the convolution layer, each layer of convolution layer has the same data distribution, improving the network The generalization ability of the network speeds up the convergence of the network, thereby improving the model training speed and recognition accuracy; the method specifically includes the following steps:

[0029] Step S1, normalize the input image to the uniform size required by the net...

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Abstract

The invention relates to a defect identification method for an electrowetting display screen. A convolution neural network with a batch normalization algorithm is added in the defect identification method for the electrowetting display screen. The convolution neural network comprises a four convolution layers, three pooling layers, three batch normalization layers, two full-connection layers, twodropout layers and an output layer. Batch normalization layers are added after the convolution layers, so that each convolution layer has the same data distribution. The generalization ability of thenetwork is improved, network convergence is accelerated, and therefore the model training speed and the defect identification precision are improved.

Description

technical field [0001] The invention belongs to the technical field of deep learning and image processing, and in particular relates to a method for identifying defects of an electrowetting display screen using a convolutional neural network added with a batch normalization algorithm. Background technique [0002] Electrowetting refers to changing the wettability of the droplet on the substrate by changing the voltage between the droplet and the insulating substrate, that is, changing the contact angle to cause the droplet to deform and displace. The so-called electrowetting display technology is a method developed by utilizing the natural forces inherent in the interface between oil and water and utilizing these forces. As a reflective display technology, electrowetting display has a reflection efficiency of more than 50 percent, is twice as bright as LCD, and can still be viewed under strong sunlight. At the same time, the electrowetting display does not require polarizer...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/08G06N3/045
Inventor 林志贤林珊玲郭太良何慧敏单升起钱明勇曾素云
Owner FUZHOU UNIV
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