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Deep learning-based rapid tunnel crack identification method

A technology of deep learning and recognition method, applied in the field of underground engineering, can solve the problem of not being able to directly obtain information about the length and width of cracks

Active Publication Date: 2018-06-29
SHANGHAI TONGYAN CIVIL ENGINEERING TECHNOLOGY CORP LTD
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Problems solved by technology

[0007] Aiming at the problem that the current crack detection method based on deep learning cannot directly obtain the length and width information of cracks, the present invention provides a fast identification method for tunnel cracks based on deep learning, which is used for tunnel crack detection to improve the efficiency of tunnel crack detection. efficiency

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  • Deep learning-based rapid tunnel crack identification method
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  • Deep learning-based rapid tunnel crack identification method

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Embodiment Construction

[0047] The present invention will be further described in detail below in conjunction with the drawings.

[0048] According to the attached figure 1 The present invention is a method for rapid identification of tunnel cracks based on deep learning, characterized in that the method for rapid identification of tunnel cracks includes the following steps:

[0049] S1. Create a deep learning image training set.

[0050] The specific operation of the step S1 is as follows:

[0051] (1) Collect tunnel images. The single-channel tunnel image is obtained by shooting the surface of the tunnel by mobile detection equipment. The resolution is generally not less than 2 million pixels, the definition of the image is high enough, and the minimum width of the crack in the image is not less than 1 pixel;

[0052] (2) Through manual screening of tunnel images, 50,000 crack images and 50,000 non-crack images are selected, and the image size is adjusted. The image format is consistent with the original t...

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Abstract

The invention relates to a deep learning-based rapid tunnel crack identification method, which mainly solves the problem that the current deep learning-based crack detection method cannot directly acquire the length and width information of cracks. The deep learning-based rapid tunnel crack identification method comprises the following steps: (S1) a deep learning image training set is created; (S2) a deep convolutional neural network model is trained; (S3) the trained convolutional neural network model is used for detecting a to-be-detected image and outputting a prediction label image; (S4) adetection result, which includes image category, coordinate information of cracks and pixel-level width values and length values of cracks, is outputted according to the prediction label image; (S5)a disease record result is outputted according to the detection result; if cracks exist in the to-be-detected image, then the image name, the coordinate information of the cracks and the actual widthvalues and length values of the cracks are recorded; and if no cracks exist in the to-be-detected image, then recording is not carried out.

Description

Technical field [0001] The invention relates to a method for quickly identifying tunnel cracks, and discloses a method for quickly identifying tunnel cracks based on deep learning, which is applied to the field of underground engineering. Background technique [0002] With the advancement of science and technology and the development of society, the construction scale of tunnel projects is increasing day by day, which greatly facilitates people's travel and life, such as subway tunnels, railway tunnels, highway tunnels, etc., and most of the completed tunnel projects have entered the maintenance stage Therefore, as the number of tunnels continues to increase, the operating status and disease detection of the tunnel structure have become particularly important. During the operation of the tunnel, due to the impact of vehicle vibration, surrounding load disturbances, and surrounding rock pressure changes, cracks will appear on the surface of the tunnel. The cracks not only cause th...

Claims

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Application Information

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IPC IPC(8): G06K9/20G06K9/34G06N3/04G06N3/08
CPCG06N3/08G06V10/22G06V10/267G06N3/045
Inventor 刘学增刘新根朱爱玺刘海波
Owner SHANGHAI TONGYAN CIVIL ENGINEERING TECHNOLOGY CORP LTD
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