Tunnel surface defect classification method based on deep convolutional neural network

Abstract

The invention relates to a tunnel surface defect classification method based on a deep convolutional neural network. The tunnel surface defect classification method comprises the following specific steps of S1, image acquisition, automatically acquiring a tunnel surface image by utilizing an image acquisition device; S2, database construction, performing image preprocessing operation on the original image, and constructing a tunnel surface defect database in a classified manner; S3, image detection and classification, constructing a deep convolutional neural network, and carrying out tunnel surface defect detection and classification. Compared with a traditional method, automatic recognition and classification of tunnel surface defects can be achieved, labor cost is greatly reduced, and defect detection efficiency and classification accuracy are effectively improved.

Description

technical field [0001] The invention belongs to the field of computer vision and image analysis, and in particular relates to a tunnel surface defect classification method based on a deep convolutional neural network. Background technique [0002] In recent years, with the rapid development of my country's social economy, the status of transportation has become more and more important, and the construction of tunnels has also been intensified. However, with the increase of use time and the influence of the environment, various defects will inevitably appear on the surface of the tunnel. Accurate and timely classification of defects can assist relevant departments to carry out targeted defect remediation programs. However, most of the traditional tunnel surface defect classification methods rely on manual visual inspection, which is time-consuming and labor-intensive. Therefore, it is of great practical significance to develop an efficient classification method for surface ...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a tunnel surface defect classification method based on a deep convolutional neural network to solve the problem that the traditional tunnel surface defect classification methods in the prior art mostly rely on manual visual inspection, which is very time-consuming and laborious.

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