A bridge crack image generation model based on a depth convolution generation antagonistic network

Abstract

The invention relates to a bridge crack image generation model based on a depth convolution generation type antagonistic network, which comprises a generation sub-model and a discrimination sub-model,and the generation sub-model and the discrimination sub-model are trained. The generating sub-model sequentially comprises a full connection layer, a dimension conversion layer, a first transposed convolution layer, a second transposed convolution layer, a third transposed convolution layer, a fourth transposed convolution layer and a fifth transposed convolution layer; the generating sub-model comprises a full connection layer, a dimension conversion layer, a first transposed convolution layer, a second transposed convolution layer, a third transposed convolution layer, and a fifth transposed convolution layer. The discriminant sub-model includes first convolution layer, second convolution layer, third convolution layer, fourth convolution layer, fifth convolution layer, sixth convolution layer, seventh convolution layer and Sigmoid activation function layer. The most generated crack image of the bridge crack image generation model of the invention is clear, basically meshless phenomenon, and extremely high similarity with the truly collected crack image. The discriminator model of the invention adds a convolution kernel of 1x1 to reduce dimensions without changing the size of the feature map, reduce the number of parameters, and thus reduce the calculation time.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a bridge crack image generation model based on a deep convolution generation confrontation network. Background technique [0002] Transportation is the basic need and prerequisite for economic development, the survival foundation and civilization symbol of modern society, the infrastructure and important link of industrial development, which is related to the development of national economy and the lifeblood of social progress. [1] . The management of highway bridges in our country has always attached importance to construction and neglected maintenance. With the continuous progress of transportation, the rapid growth of highway traffic flow has brought huge pressure to the operation safety of highway bridges. Such pressure will lead to faster bridge construction. With the speed of road aging, it poses a safety hazard. The quality and safety of bridges is re...

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Problems solved by technology

However, the DCGAN model in the literature [Radford A, Metz L, Chintala S.Unsupervised representation learning with deep convolutional generative adversarial networks[J].Computer Science,2015.] is suitable for generating images of 64x64 size similar to cifar-10 and SVHN datasets , for the 256x256 size, the generation of bridge pavement crack images with linear topology can not achieve satisfactory results, even can not be generated

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