Panoramic image CNN based tunnel disease automatic identification device

A tunnel disease and automatic identification technology, applied in the direction of measuring devices, material analysis through optical means, instruments, etc., to achieve the effect of improving the level of automation and intelligence, good universality, solving complexity and popularization

Inactive Publication Date: 2017-06-13
ZHEJIANG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

[0013] Aiming at the shortcomings of the current tunnel lining disease detection process, such as the difficulty of quickly and conveniently obtaining the panoramic image of the inner wall of the tunnel and the difficulty of automatically detecting and identifying various diseases, the present invention provides a tunnel disease automatic identification device based on panoramic image

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  • Panoramic image CNN based tunnel disease automatic identification device
  • Panoramic image CNN based tunnel disease automatic identification device
  • Panoramic image CNN based tunnel disease automatic identification device

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Embodiment Construction

[0098] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0099] refer to Figure 1 to Figure 6 , an automatic identification device for tunnel defects based on panoramic vision CNN, including a tunnel inspection vehicle and a remote computer. figure 1 Schematic diagram of ODVS performing panoramic visual inspection on the inner wall of the tunnel. The gray area in the picture is the part where ODVS acquires a 360° omnidirectional image of the inner wall of the tunnel.

[0100] The tunnel detection vehicle is equipped with an active panoramic vision sensor, an RFID reader, a distance measuring wheel, a wireless transmission unit, a controller and an industrial computer, and the active panoramic vision sensor is installed on the tunnel detection In the center of the front of the car, the RFID reader reads the RFID information arranged on the inner wall of the tunnel, and a distance measuring whe...

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Abstract

The invention discloses a panoramic image CNN based tunnel disease automatic identification device. According to the device, firstly, a panoramic image of a tunnel inner wall is quickly acquired with a panoramic vision sensor; then the panoramic image is processed mainly by panorama unrolling, image preprocessing, binarization processing and the like to extract suspected disease region; and finally, the disease is subjected to automatic detection and classification identification by adopting a convolutional neural network. According to the scheme, the structure of the detection device in extracting a tunnel inner wall panoramic image is greatly simplified, and various tunnel disease characteristics can be automatically extracted, detected and identified by the end-to-end convolutional neural network, and an effective technical support is provided to tunnel maintenance and completion acceptance.

Description

technical field [0001] The present invention relates to the application of omni-directional visual sensor, pattern recognition, artificial intelligence, applied mathematics, digital image processing and computer vision technology in the detection of tunnel damage, in particular to a tunnel fault automatic identification device based on panoramic image CNN. Background technique [0002] In order to solve the pressure brought by the population flow and the relative concentration of employment points on traffic and the environment, and to meet the needs of the national environment and situation changes, various tunnels and underground projects (such as urban subways, road tunnels, railway tunnels, underwater tunnels, etc.) Tunnels, municipal pipelines, underground energy caverns, etc.) have become an inevitable trend. [0003] As of 2014, there were 12,404 highway tunnels across the country with a length of 10.7567 million meters. Among them, there are 626 extra-long tunnels, ...

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Application Information

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IPC IPC(8): G01N21/88
CPCG01N21/8851G01N2021/8887
Inventor 汤一平胡克钢袁公萍吴挺鲁少辉韩国栋陈麒何霞陈朋王丽冉
Owner ZHEJIANG UNIV OF TECH
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