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Bridge crack detection and classification method based on conditional filtering GAN

A classification method and conditional technology, applied in the direction of computer parts, instruments, characters and pattern recognition, etc., can solve problems such as difficult classification of cracks, and achieve the effect of accurate and efficient classification

Inactive Publication Date: 2019-08-20
HUBEI UNIV FOR NATITIES
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] In short, there are many kinds of features used to detect cracks, but simple and efficient detection and then crack classification is a difficult point

Method used

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  • Bridge crack detection and classification method based on conditional filtering GAN
  • Bridge crack detection and classification method based on conditional filtering GAN
  • Bridge crack detection and classification method based on conditional filtering GAN

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Embodiment Construction

[0035] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0036] Wherein, the accompanying drawings are for illustrative purposes only, and represent only schematic diagrams, rather than physical drawings, and should...

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Abstract

The invention relates to a bridge crack detection and classification method based on a conditional filtering GAN network, and relates to the technical field of crack detection, and the method comprises the following steps: S1, selecting a proper amount of pictures according to the danger levels of three types of bridges, adding corresponding labels, selecting image pixels according to actual conditions, and establishing a database, the danger levels of the three types of bridges comprising linear cracks, blocky cracks and net-shaped cracks; S2, establishing a conditional filtering GAN network;S3, training a discriminator D and a generator G, fixing one party in the training process, updating the network weight of the other party, and performing alternate iteration; and S4, inputting a to-be-classified picture to the conditional filtering GAN network, and outputting a corresponding tag to obtain a classification result.

Description

technical field [0001] The invention belongs to the technical field of bridge crack classification, and relates to a bridge crack detection and classification method based on conditional filter GAN. Background technique [0002] With the rapid development of my country's economic development, urbanization process, and national projects such as high-speed rail, in the construction of roads, railways, and urban and rural water conservancy, the number of bridges built to leap over obstacles is increasing day by day. Bridges play an important role in the development of the national economy. It plays a pivotal role and is also a manifestation of our country's comprehensive strength. Due to the ubiquity of bridges, the structure and durability of bridge bodies cannot be ignored. Cracks, as a main structural disease feature of the bridge body, are the most harmful to the durability and safety of the bridge body structure. Therefore, cracks make it one of the main evaluation indicat...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24
Inventor 郭黎陈锦皓廖宇姚红英吕彬骑李晓艳
Owner HUBEI UNIV FOR NATITIES
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