Simulation experiment device for intelligent detection of tunnel lining defects based on image recognition

Abstract

The invention discloses an image recognition-based intelligent detection simulation experiment device for tunnel lining defects. Each loading tray of the circulating loading tray is placed in the chute of the sliding rail of the circulating loading tray. The circular information wall is sequentially fixed on the inner ring edge of each loading tray. The dial is driven by a motor, and the gear teeth of the dial fluctuate and each load tray of the cyclic load tray slides cyclically on the chute, and at the same time drives the cyclic information wall to move synchronously. The background cloth is supported by the background cloth bracket and hangs vertically in the center of the oval track to form the camera background curtain wall. The camera equipment is placed outside the sliding rail of the circular loading tray, and the height of the lens is adapted to the circular information wall. The present invention utilizes the relative motion relationship to convert the camera to be continuously moved along the tunnel to obtain pictures in actual engineering, and convert the camera to be fixed while the diseased test block or paper tape continues to move. It greatly reduces the site space needed to create a continuous relative movement of the camera and the observed object.

Description

Technical field [0001] This patent relates to a tunnel lining disease information intelligent testing experiment device, specifically an image-recognized tunnel lining disease intelligent detection simulation experimental device for development, improvement and effect inspection of tunnel lining disease machine visual inspection system . Background technique [0002] In recent years, the construction and research of tunnel projects such as roads, railways and urban rail transit has been developed rapidly. The development and application of tunnel informationization, science, and convenient maintenance testing systems have also gradually be emphasized. [0003] Common tunnel diseases include the following categories: tunnel leakage, lining corrosion, lining, tunnel frozen damage, cave door damage, overall bed crack loss, damage to the affiliated structure. According to many tunnel diseases, the damage and leakage of lining structures are the main factors affecting the safety of tu...

AI Technical Summary

Problems solved by technology

As a relatively mature technology, the non-contact measurement method of the total station has certain reliability, but it also has shortcomings: the measurement accuracy is not as good as the traditional measurement method, and the accuracy of the data is also affected by the operation of the operator. There are manual errors and The error caused by the instrument itself and other influences of the field environment

Although these two methods are closest to the actual application environment, they require researchers to transport all the manufactured equipment to the tunnel project site, which consumes manpower, material resources and time. Moreover, the tunnel site environment is complex and harsh, and environmental variables are difficult to control one by one. If the experimental results The expected goal was not achieved, and it is difficult to judge the specific reasons, so we propose a laboratory research device for the image recognition system of tunnel defects

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