Tunnel image boundary line detection method based on reflectivity intensity

Abstract

The invention discloses a tunnel image boundary line detection method based on reflectivity intensity, and the method comprises the steps: scanning a tunnel through a three-dimensional laser scanner, and obtaining the original three-dimensional point cloud data of the tunnel; extracting the central axis of the tunnel based on the original three-dimensional point cloud data of the tunnel; expanding the original three-dimensional point cloud data of the tunnel based on the extracted central axis of the tunnel, and projecting the original three-dimensional point cloud data to a parameter plane to form a tunnel reflectivity image; adjusting the intensity value of each pixel of the tunnel reflectivity image by using the local information of the tunnel reflectivity image so as to enhance the boundary characteristics of the tunnel reflectivity image; and based on the enhanced tunnel reflectivity image, extracting boundary line features in the image. According to the method, the reflectivity intensity information of the local area in the tunnel reflectivity image can be processed, and the boundary characteristics in the tunnel image can be obviously enhanced, so that the accuracy of tunnel boundary line extraction is further improved, and an important foundation is laid for tunnel deformation analysis.

Description

technical field [0001] The invention relates to the technical field of tunnel detection, in particular to a tunnel image boundary line detection method based on reflectivity intensity. Background technique [0002] As urban rail transit becomes more and more popular, its safety is also paid more and more attention. Among them, tunnel traffic plays an important role in urban rail transit. However, due to the continuous impact of underground pressure and road construction, subway tunnels may deform, causing problems such as water seepage, cracks and falling blocks. These problems will not only affect the reliability of subway operation, but also cause major safety accidents. Therefore, timely and accurate detection of subway tunnels is an important basis for ensuring the safe operation of tunnel traffic. [0003] With the wide application of 3D laser scanning technology in the tunnel field, the 3D point cloud and laser reflectivity images of the tunnel have improved the eff...

AI Technical Summary

Problems solved by technology

However, due to the inconspicuous structural characteristics of the tunnel boundary line and the large amount of noise data generated during the scanning process, the efficiency and accuracy of boundary line extraction and tunnel structure deformation detection are seriously affected.

At present, although there are some methods that can extract the obvious boundary line features in actual images, for tunnel images with inconspicuous boundary line features, the existing methods still cannot extract accurately, because the boundary line features in tunnel images and image The contrast of the background is small and the noise level is high, the existing methods cannot accurately identify and detect, and ultimately cannot improve the detection efficiency of the boundary line

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