Diaphragm flaw detection method based on reverse bottleneck structure deep convolutional network

A deep convolution and point detection technology, which is applied in the field of deep learning and computer vision, can solve the problems of low recognition accuracy and slow speed, and achieve the effects of improving accuracy, reducing the amount of parameters, and reducing costs

Inactive Publication Date: 2019-11-05
HOHAI UNIV CHANGZHOU
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Problems solved by technology

[0004] Purpose of the invention: In order to solve the problems of low recognition accuracy and slow speed of the traditional flaw detection method, the present invention provides a membrane flaw detection method based on the reverse bottleneck structure deep convolution network, by using the reverse bottleneck structure deep convolution network The product network realizes the detection and calibration of the defect points in the diaphragm

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  • Diaphragm flaw detection method based on reverse bottleneck structure deep convolutional network
  • Diaphragm flaw detection method based on reverse bottleneck structure deep convolutional network
  • Diaphragm flaw detection method based on reverse bottleneck structure deep convolutional network

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Embodiment

[0052] Such as figure 1 As shown, a membrane defect detection method based on the reverse bottleneck structure deep convolutional network.

[0053] Step 1. Prepare the dataset

[0054] The images of the diaphragm with defects are collected from the production workshop to make a data set, which contains several pictures taken by industrial-grade cameras.

[0055] Step 2: Segment the collected defective diaphragm images into a series of small images.

[0056] Since the collected initial image is very large, and the defect points in it are very small, if the large image is directly input into the network for training, it will not only increase the difficulty of training and detection, but also cause a waste of computing resources, and it is difficult to analyze the data. Sets are labeled. Therefore, pixel-level cropping is performed on the original image, and each original image can be cropped into 800 small images of 224×224 pixels.

[0057] Step 3: Judging and labeling the ...

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Abstract

The invention discloses a diaphragm flaw detection method based on a reverse bottleneck structure deep convolutional network, and the method achieves the detection and calibration of flaws in a diaphragm through employing the reverse bottleneck structure deep convolutional network. The method comprises the steps of image acquisition, image segmentation, data annotation, network training, flaw detection, image splicing and the like. According to the method provided by the invention, the effectiveness of the deep convolutional network for image feature extraction is fully utilized, and the number of parameters can be greatly reduced by the reverse bottleneck structure under the condition that the detection precision is ensured, so that the purpose of quickly and accurately detecting the defect points in the diaphragm is achieved.

Description

technical field [0001] The invention relates to the fields of deep learning and computer vision, and specifically designs a membrane defect detection method based on a reverse bottleneck structure deep convolution network. Background technique [0002] With the rapid development of the electronic technology industry, various portable devices such as notebook computers, tablet computers, and mobile phones are widely used in daily life, and the display screen, the window of the human-computer interaction interface, is particularly important. Due to the advantages of high display quality, no electromagnetic radiation, wide viewing area, and low power consumption, LCD screens are used in almost all portable devices. The optical film on the LCD screen not only protects the LCD screen but also affects the clarity of the display. In the production process of the optical film, dust, scratches, uneven printing, etc. will cause defects in the optical film, and these defects directly ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/62G06N3/04
CPCG06T7/0004G06N3/045G06F18/214
Inventor 徐宁叶晓伟刘小峰姚潇
Owner HOHAI UNIV CHANGZHOU
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