Fine crack segmentation method based on generative adversarial networks

A network and crack technology, applied in biological neural network models, image analysis, image data processing, etc., can solve the problems that feature detection cannot be guaranteed effectively, training and testing take a lot of time, and achieve the effect of high graphics quality.

Inactive Publication Date: 2019-03-29
SHAANXI NORMAL UNIV
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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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  • Fine crack segmentation method based on generative adversarial networks
  • Fine crack segmentation method based on generative adversarial networks
  • Fine crack segmentation method based on generative adversarial networks

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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 fine crack segmentation method based on generative adversarial networks, comprising the following steps: step one, preparing a plurality of crack images; 2, training that generator network to calculate the pixel loss; 3, training that segmentation branch of the discriminator and calculating the segmentation loss; 4, respectively reading that pixel loss and the segmentation los, on the basis of which the discriminating branch of the generator and the discriminator are jointly trained to calculate the antagonism loss; The method combines the super-resolution image reconstruction and semantic segmentation of the generated countermeasure network to design a new segmented generated countermeasure network. Compared with the traditional super-resolution image generationalgorithm, the super-resolution fine crack image quality of the invention is higher, and is more similar to the original high-resolution image.

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 Applications(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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