A detection method for segment error in subway tunnel

Abstract

The invention discloses a subway tunnel segment dislocation quantity detection method. The subway tunnel segment dislocation quantity detection method includes the following steps of: inputting a three-dimensional data matrix of a depth image acquired by a Kinect device, and performing pre-treating on data; converting the depth image into a binary image capable of being processed by a data image technology, through a double diagonal difference algorithm; processing noise in the binary image through a combined de-noising method, and meanwhile, removing special noises from bolt holes and injected holes based on shape features; extracting a dislocation framework though a thinning algorithm, identifying different dislocation lines through a global search algorithm, and finding dislocation positions in the corresponding depth image to calculating the segment dislocation quantity. According to the subway tunnel segment dislocation quantity detection method, the calculation is simple, the operation time is short, and manpower is not required; the detection of the segment dislocation quantity can be completed only through inputting the acquired depth image of a subway tunnel segment by means of surface measurement, thus, the detection method is high in efficiency and is accurate in detection.

Description

technical field [0001] The invention relates to the field of detection of subway tunnel segments, in particular to a method for detecting the amount of misalignment of subway tunnel segments. Background technique [0002] The segment misalignment of a subway tunnel refers to the elevation difference between two adjacent segments in a subway tunnel. The influencing factors of tunnel segment misalignment are quite rich and complex, such as irregular assembly, improper grouting control, improper attitude control of the shield machine, etc., which can easily cause accidents such as tunnel water leakage and segment cracking. As the damage continues to deepen, there may be a large amount of sediment influx and a large amount of water in the interior of the segment. In actual engineering, it is difficult for engineering inspectors to discover the above problems at the first point in time when segment misalignment occurs, which leads to further aggravation of the misalignment, whic...

AI Technical Summary

Problems solved by technology

Slow speed, heavy traffic interference, and low precision; the existence of the wrong platform in the automatic detection method will significantly affect the characteristics of the International Roughness Index (IRI). There are laser profilers, ultrasonic profilers, etc., and the profilers are expensive and cannot be right or wrong. Accurate measurement

In summary, the existing detection technology has problems such as large error, low efficiency, and high cost. It is very necessary to study a high-efficiency, accurate detection algorithm for the detection of segment error in subway tunnels.

Images