Tunnel detection method and device based on unmanned aerial vehicle

A detection method, UAV technology, applied in computer parts, image analysis, instruments, etc., can solve problems such as recognition rate uncertainty

Active Publication Date: 2021-08-20
SHENZHEN UNIV
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Problems solved by technology

[0006] The main purpose of the present invention is to provide a tunnel detection method and device based on drones, aiming to solve the problem that traditional disease inspection mainly relies on visual inspection to determine the type of disease initially, and the recognition rate depends on the experience of the inspectors, and the recognition rate has large discrepancies. question of certainty

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  • Tunnel detection method and device based on unmanned aerial vehicle
  • Tunnel detection method and device based on unmanned aerial vehicle
  • Tunnel detection method and device based on unmanned aerial vehicle

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[0040] In order to make the object, technical solution and advantages of the present invention more clear and definite, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0041] Convolutional neural network is a kind of neural network method based on convolution operation, which is one of the representative networks of deep neural network. Convolutional neural network has the characteristics of parameter sharing, local perception and multi-core, and uses multiple volumes The product kernel is used for calculation, which can effectively extract different features. 3D-CNN usually consists of 3-dimensional convolution kernels. Two-dimensional convolution (CNN, Convolutional Neural Networks, Convolutional Neural Networks) is convolution in spatial dimensions,...

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Abstract

The invention discloses a tunnel detection method and device based on an unmanned aerial vehicle. The method comprises the following steps: receiving a video image of a tunnel acquired by the unmanned aerial vehicle; performing preprocessing and standardization processing on the video image; and inputting the preprocessed and standardized video image into a trained three-dimensional convolutional neural network (3D-CNN) model, wherein the three-dimensional convolutional neural network model outputs a tunnel disease detection result according to the video image. According to the method, the video image of the tunnel is acquired through the unmanned aerial vehicle, and the video image is input into the trained three-dimensional convolutional neural network model, so that the space and time features in the video can be better captured, the tunnel disease detection result is quickly output, the recognition rate, the detection frequency and the processing speed of the tunnel disease are improved, and the manual dependence degree is reduced.

Description

technical field [0001] The invention relates to the technical field of tunnel detection, in particular to a tunnel detection method and device based on an unmanned aerial vehicle. Background technique [0002] With the acceleration of urbanization, urban subways have developed rapidly, and more and more underground infrastructures have been built accordingly. In recent years, tunnel safety problems have occurred frequently, such as tunnel cracks, water leakage, settlement, lining peeling, falling blocks, etc., causing serious casualties and huge economic losses. Therefore, tunnel safety is a key issue during tunnel operation. [0003] Traditional manual inspection and closed-circuit television system will not find problems in time, which will lead to serious consequences. Tunnel disease inspection should be carried out by combining manual and information means. With the continuous development of UAV technology, UAV application fields are becoming more and more extensive, an...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/00G06N3/04
CPCG06T7/0002G06V20/10G06V20/40G06N3/045
Inventor 包小华李颖鹏崔宏志陈湘生贾金青
Owner SHENZHEN UNIV
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