Crack identification method based on deep learning

A technology of crack identification and deep learning, applied in the field of crack identification based on deep learning, can solve the problems of poor recognition efficiency, accuracy and adaptability, and achieve the effect of solving the shortage of training set data samples and improving recognition efficiency

Pending Publication Date: 2021-12-21
CENT SOUTH UNIV
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[0006] In view of this, the present application provides a crack identification method based on deep learning, which

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  • Crack identification method based on deep learning
  • Crack identification method based on deep learning
  • Crack identification method based on deep learning

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[0058] The present application will be described in detail below with reference to the accompanying drawings.

[0059] The embodiments of the present application will be described below by way of specific examples, and those skilled in the art can easily understand other advantages and efficacy of the present invention. Obviously, the described embodiments are merely the embodiments of the present invention, not all of the embodiments. The present application can also be implemented or applied by other specific embodiments, and the details in this specification can also be made based on different perspectives and applications, and various modifications or changes are carried out from the spirit of the present application. It should be noted that the features in the following examples and embodiments may be combined with each other in the case of unlilled. Based on the embodiments in the present application, all other embodiments obtained without creative labor are not made in the ...

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Abstract

The invention provides a crack identification method based on deep learning, and belongs to the technical field of image processing, and the method specifically comprises the steps: constructing a deep convolutional adversarial network; acquiring a plurality of real crack image samples to train the deep convolutional adversarial network to obtain an adversarial crack image sample; obtaining a mixed crack image sample; labeling all the mixed crack image samples, and taking the labeling information file and the mixed crack image samples as a training set; training the improved YOLOv4 neural network by adopting the training set until the network converges, and storing convergent network parameters as a crack identification model; and inputting the acquired to-be-detected crack image into the crack identification model, and outputting identification information. Through the scheme of the invention, the deep convolutional adversarial network is constructed to realize the automatic generation of the crack, and the crack recognition model is obtained through the improved YOLOv4 neural network training to recognize the to-be-detected crack image, so that the recognition efficiency, accuracy and adaptability are improved.

Description

technical field [0001] The present application relates to the technical field of image processing, and in particular to a crack recognition method based on deep learning. Background technique [0002] At present, with the steady improvement of my country's economy, the speed and scale of infrastructure construction have reached a new climax. During the service of infrastructure, due to the joint influence of long-term loads, geological disasters, human activities and other internal and external factors, it is inevitable that various structural diseases will appear, among which cracks appear most frequently. If the treatment is carried out, it is very likely to cause hazards such as water leakage, concrete corrosion, and reduction of structural bearing capacity, which will affect the safety of the structure during operation. Therefore, it is necessary to regularly detect cracks in the infrastructure, grasp the number and specific locations of cracks, and take corresponding m...

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Application Information

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IPC IPC(8): G06T7/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06T7/0004G06N3/08G06N3/048G06N3/045G06F18/241
Inventor 周中张俊杰李守文胡江锋邓卓湘龚琛杰鲁四平
Owner CENT SOUTH UNIV
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