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Small crack segmentation method based on generative adversarial network

A generative, network technology, applied in biological neural network models, image analysis, image enhancement, etc., can solve the problems of inability to guarantee the effectiveness of feature detection, training and testing time-consuming, etc., and achieve the effect of high graphics quality.

Inactive Publication Date: 2021-09-03
SHAANXI NORMAL UNIV
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AI Technical Summary

Problems solved by technology

However, the method of data augmentation often results in a large amount of time for training and testing
However, the method of constructing high-level features from low-level features cannot guarantee that the constructed features are effective for the final detection, and its contribution to the detection effect is limited to repaying the calculation cost

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  • Small crack segmentation method based on generative adversarial network
  • Small crack segmentation method based on generative adversarial network
  • Small crack segmentation method based on generative adversarial network

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Embodiment Construction

[0045] In order to further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the specific implementation, structural features and effects of the present invention will be described in detail below in conjunction with the accompanying drawings and examples.

[0046] The generative confrontation network consists of two parts, the generator and the discriminator. The discriminator is a simple convolutional neural network model, which takes real images and fake images constructed by the generator as input, and extracts features from the input data through a series of convolutional layers, excitation layers, normalization layers, and pooling layers , and finally output the probability value of the [0, 1] interval; the generator is a reverse convolutional neural network model, through a series of deconvolution layers for upsampling, combined with the excitation layer, the low-dimensional vector is converted into a real Vecto...

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Abstract

The invention relates to a small crack segmentation method based on a generative confrontation network, comprising the following steps: step 1, preparing several crack images; step 2, training a generator network, and calculating pixel loss; step 3, training a segmentation branch of a discriminator , calculate the segmentation loss; step 4, read the pixel loss and segmentation loss respectively, on this basis, jointly train the discrimination branch of the generator and the discriminator, and calculate the confrontation loss; the fine crack segmentation method of the present invention uses the generative confrontation network Compared with the traditional super-resolution image generation algorithm, the super-resolution image quality of the present invention is higher, and it is similar to the original Higher resolution images are more similar.

Description

technical field [0001] The invention belongs to the technical fields of computer vision, digital image processing and machine learning, and in particular relates to a small crack segmentation method based on a generative confrontation network. Background technique [0002] With the development of the transportation industry, road maintenance has become very important. As an important part of the transportation hub in today's society, the bridge not only bears the heavy responsibility of transportation but also concerns the safety of the transportation personnel. However, due to the fact that the bridge structure will inevitably suffer from various damages during long-term use, it will cause the resistance of the bridge structure to attenuate and cause safety hazards. Therefore regular inspection and maintenance is essential. Cracks are the most common defect in bridges. Cracks in bridges can occur for a variety of reasons, mainly due to fatigue of the asphalt pavement, com...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/10G06N3/04
CPCG06T7/10G06T2207/20081G06T2207/20084G06N3/045
Inventor 李良福胡敏
Owner SHAANXI NORMAL UNIV
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