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A tunnel detection method and device based on unmanned aerial vehicles

A detection method and unmanned aerial vehicle technology, applied in computer components, image analysis, instruments, etc., can solve problems such as uncertainty of recognition rate, achieve the effect of reducing manual dependence and improving recognition rate

Active Publication Date: 2021-11-30
SHENZHEN UNIV
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AI Technical Summary

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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  • A tunnel detection method and device based on unmanned aerial vehicles
  • A tunnel detection method and device based on unmanned aerial vehicles
  • A tunnel detection method and device based on unmanned aerial vehicles

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Embodiment Construction

[0040] In order to make the objects, technical solutions and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are intended to explain the present invention and is not intended to limit the invention.

[0041] Convolutional neural networks are a class of neural network based on convolutional calculations, one of the representative networks of deep neural networks, convoluting neural networks have parameter sharing, topical perception and multi-core nature, using multiple volumes The nuclear can be calculated, and different feature can be effectively extracted. 3D-CNN is usually convolved by 3 dimensions, two-dimensional disinvolution (CNN, CONVOLUTIONAL NEURAL NETWORKS, convolutional neural network) is a convolution in spatial dimensions, and three-dimensional volume is both a convolution in space. Convolution on time. CNN is...

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Abstract

The invention discloses a tunnel detection method and device based on an unmanned aerial vehicle. The method includes: receiving a video image of a tunnel obtained by an unmanned aerial vehicle; performing preprocessing and standardization processing on the video image; The processed video image is input into a trained three-dimensional convolutional neural network (3D-CNN) model, and the three-dimensional convolutional neural network model outputs a tunnel defect detection result according to the video image. The invention obtains the video image of the tunnel through the drone, and inputs the video image into the trained three-dimensional convolutional neural network model, which can better capture the spatial and temporal characteristics of the video, and quickly output the tunnel defect detection result , improve the identification rate, detection frequency and processing speed of tunnel diseases, and reduce the degree of manual dependence.

Description

Technical field [0001] The present invention relates to the field of tunneling detection, and more particularly to a drone-based tunnel detection method and apparatus. Background technique [0002] With the acceleration of urbanization, urban subways have developed rapidly, and the underground infrastructure of construction is also more and more. In recent years, tunnel safety problems have been frequent, such as tunnel cracks, leakage, settling, lining, causing serious casualties and huge economic losses, so tunnel safety is a key issue during tunnel operations. [0003] Traditional artificial inspections and closed-circuit television systems discovering that problems will lead to serious consequences, and tunnel disease inspections should be carried out in a manner in combination with information. With the continuous development of drone technology, the field of drone applications is increasingly wide. It has been applied in aerial photography, agriculture, plant protection, ex...

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Application Information

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Patent Type & Authority Patents(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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