Crack identifying method based on deep learning convolutional neural network

A convolutional neural network and deep learning technology, applied in biological neural network models, neural architecture, character and pattern recognition, etc., can solve problems such as errors and a large number of sensors, improve efficiency, reduce image quality requirements, reduce labor The effect of workload

Active Publication Date: 2017-12-15
ZHEJIANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Although they introduced machine learning algorithms, these methods inevit

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  • Crack identifying method based on deep learning convolutional neural network
  • Crack identifying method based on deep learning convolutional neural network

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Embodiment Construction

[0032] The following combination figure 1 The target image acquisition schematic shown in and figure 2 The implementation flow chart shown in, takes a bridge as an example (actually applicable to various structures), and further sets forth the specific embodiment of the present invention.

[0033] illustration: figure 1 The codes in represent respectively:

[0034] 1 - target structure;

[0035] 2——Cracks on the surface of the target structure;

[0036] 3 - No cracks on the surface of the target structure;

[0037] 4——There is a crack area on the surface of the target structure;

[0038] 5 - digital camera;

[0039] Remarks: The images collected in the present invention should include images collected under various actual conditions such as different light and shade and light intensity.

[0040] A method for identifying cracks based on a deep convolutional neural network according to the present invention, the specific steps are as follows:

[0041] A. Collect images ...

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Abstract

The invention proposes a crack identifying method based on deep learning convolutional neural network, and the method comprises the following specific steps: A) collecting an image building, training and verifying database; B) establishing a deep learning convolution neural network; and C) using the image database in A to train the completed deep learning neural network in B.

Description

technical field [0001] The invention relates to a method for identifying cracks in an image using a deep learning convolutional neural network. Background technique [0002] Civilian infrastructure such as bridges, dams, and skyscrapers gradually degrade over time and lose their ability to function as designed. Concrete cracks or cracks in steel structures are one of the key diseases of such facilities. Although people have increased the detection of these facilities, it is necessary to close traffic or block buildings during on-site inspections. At the same time, manual detection of large areas of structural surfaces is inefficient. , it is difficult to detect timely and accurately in the face of a large number of infrastructures. [0003] Many experts have proposed various methods based on visual technology to identify damage to detect cracks on the surface of various structures. The core of these methods is image processing technology. One of the important advantages of...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06N3/04
CPCG06V20/176G06V10/267G06N3/045
Inventor 叶肖伟金涛陈鹏宇
Owner ZHEJIANG UNIV
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