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Deep learning-based tunnel lining disease intelligent detection method

A deep learning and intelligent detection technology, applied in the field of tunnel disease detection, can solve the problems of strong subjectivity and low efficiency of diseases, and achieve the effect of improving work efficiency

Inactive Publication Date: 2022-07-12
CHONGQING UNIV
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Problems solved by technology

[0004] In view of this, the purpose of the present invention is to provide an intelligent detection method for tunnel lining diseases based on deep learning, so as to solve the technical problems of strong subjectivity and low efficiency in the prior art by manual identification of diseases

Method used

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  • Deep learning-based tunnel lining disease intelligent detection method
  • Deep learning-based tunnel lining disease intelligent detection method
  • Deep learning-based tunnel lining disease intelligent detection method

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Embodiment Construction

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0033] As shown in the figure, the intelligent detection method for tunnel lining diseases based on deep learning in this embodiment includes the following steps:

[0034] S1: Collect the images of the tunnel vault, arch waist and side wall, and then select the images with diseases through manual detection, crop the disease images, and mark the disease category and location information, and then make a tunnel lining composed of the marked disease image samples. Disease dataset.

[0035] In this step, the tunnel inspection vehicle is specifically used to conduct fast and uninterrupted inspection of multiple tunnels. The inspection vehicle uses three cameras to scan the tunnel lining. The three cameras collect images of the tunnel vault, arch waist and side wall respectively. The cameras use 4K line scanning. The camera adopts the half-frame mode acquisiti...

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Abstract

The invention discloses a tunnel lining disease intelligent detection method based on deep learning. The method comprises the steps that S1, a tunnel lining disease data set composed of marked disease image samples is made; s2, dividing disease image samples in the tunnel lining disease data set into a training set and a test set; s3, building a deep learning model; s4, training and parameter iteration are carried out on the built deep learning model through the training set, the detection effect of the trained deep learning model is evaluated through the test set, and the finally applied deep learning model is selected; and S5, detecting diseases in the tunnel image by using the selected deep learning model, and outputting disease category and position information. The tunnel image is used as a data source, the tunnel apparent diseases are intelligently identified and classified through the deep learning model, the disease positions are positioned, and the working efficiency is greatly improved compared with the existing manual judgment and assessment of the diseases.

Description

technical field [0001] The invention relates to the technical field of tunnel disease detection, in particular to a tunnel lining disease detection method. Background technique [0002] Affected by construction quality, operating years and external environment, tunnels will cause cracks, deformation, damage, block drop and water leakage and other diseases, which threaten the safety, stability and durability of the tunnel structure. [0003] The type and development of the disease are important indicators for tunnel safety evaluation. At present, the detection of disease mainly relies on manual discrimination and evaluation, which is highly subjective and low in efficiency. It is imminent for the intelligent, continuous and rapid identification of tunnel diseases. SUMMARY OF THE INVENTION [0004] In view of this, the purpose of the present invention is to provide an intelligent detection method for tunnel lining diseases based on deep learning, so as to solve the technical...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06V10/25G06V10/774G06K9/62G06V10/82G06N3/04G06N3/08
CPCG06T7/0002G06N3/04G06N3/084G06F18/214
Inventor 李眉慷涂歆玥朱倩雯
Owner CHONGQING UNIV
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