A Data Enhancement Method of Dam Crack Image Based on Deep Convolutional Generative Adversarial Network

A technology of image data and depth convolution, applied in image enhancement, image data processing, image analysis, etc., can solve problems such as low image quality, inconspicuous enhancement effect, failure to meet category and data volume requirements, etc., to achieve improved quality, increasing the number of channels, and mitigating the effects of performance degradation

Active Publication Date: 2021-07-27
HOHAI UNIV +3
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Problems solved by technology

Common data enhancement methods include flipping, rotating, zooming, cropping, translating, random data erasure, etc., but the enhancement effect is not obvious, and the quality of the enhanced image is not high, which cannot meet the requirements of the training sample category in the dam crack image recognition technology. and data volume requirements

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  • A Data Enhancement Method of Dam Crack Image Based on Deep Convolutional Generative Adversarial Network
  • A Data Enhancement Method of Dam Crack Image Based on Deep Convolutional Generative Adversarial Network

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[0025] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0026] On the basis of basic data enhancement operations on the dam crack image data, the real crack image data of the dam is used as input, and an image generator (DGM) based on a deep convolutional generative confrontation network is used to generate the dam crack image. device structure such as figure 1 As shown, an image discriminator (DDM) based on a deep convolutional generative confrontation network is used to judge the quality of the image generated by the DGM. The structure of the discrim...

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Abstract

The invention discloses a method for enhancing dam crack image data based on deep convolution generation confrontation network. The steps are as follows: (1) taking the real crack image data of the dam as input to generate a dam crack image; (2) performing steps ( The dam crack image generated in 1) is used for evaluation. On the basis of the basic data enhancement operation, the present invention utilizes an image generator (DGM) based on a deep convolution generation confrontation network to generate crack images, and utilizes an image discriminator (DDM) based on a depth convolution generation confrontation network to discriminate the generated image quality, A new dam crack sample data set is generated based on the original small-sample dam crack data, which meets the requirements for training sample category and data volume in dam crack image recognition technology.

Description

technical field [0001] The invention belongs to the technical field of engineering safety monitoring, in particular to a dam crack image data enhancement method based on a deep convolution generation confrontation network. Background technique [0002] During long-term operation, hydraulic structures are affected by various factors such as light, water impact, and temperature changes, and are prone to functional degradation and surface cracks. These problems may affect the normal operation of the dam. Dam crack detection is an important way to discover hidden dangers of dam safety in time. Traditional image recognition of dam cracks mainly relies on human vision, but the efficiency and accuracy of crack detection and recognition will decline during long-term work. With the development of digital image processing technology, the traditional manual image crack detection method is gradually replaced by the crack detection method based on machine vision, and has become one of t...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
CPCG06T5/00G06T2207/10004G06T2207/20081G06T2207/20084
Inventor 程杨堃李艳伟毛莺池许后磊葛恒于青坤字林吕昂晋良军陈亚军
Owner HOHAI UNIV
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