Tunnel disease detection method fusing laser point cloud and panoramic image

Abstract

The invention provides a tunnel disease detection method fusing a laser point cloud and a panoramic image, and relates to the technical field of tunnel detection. The method comprises: S1, scanning a tunnel section contour through a laser scanner to obtain point cloud features of tunnel apparent diseases, and recording a panoramic image of a water conveyance tunnel through a panoramic camera to obtain image features of the tunnel apparent diseases; S2, extracting the point cloud features and the image features of the tunnel apparent diseases; S3, fusing the point cloud features and the image features, establishing a corresponding relation between points in the point cloud and pixels in the image, and transmitting a candidate disease area in the point cloud to the image; and S4, taking the candidate disease area in the image as a crack seed point, filling the disease area and an interference area in the image with the crack seed point through a growth and communication algorithm, and inputting the point cloud features and the image features of the disease area and the interference area into a classifier for classification and identification to identify the crack.

Description

technical field [0001] The present invention relates to the technical field of tunnel detection, in particular, the present invention relates to a method for detecting tunnel defects by fusing laser point clouds and panoramic images. Background technique [0002] The long-distance water transfer tunnel is the main building of the water diversion project. In the process of construction and use, due to rock formation properties, temperature stress, water erosion and other reasons, diseases and aging defects will inevitably occur. The disease of the water transfer tunnel affects the internal safety of the tunnel. With the passage of time, it may further intensify, and the initial small cracks will gradually develop and expand. If the small cracks cannot be detected and repaired in time, it will cause accidents such as fracture and collapse of water delivery tunnels, pipeline settlement, and pressurized pipeline bursts. , threaten the stability and development of the city, and t...

AI Technical Summary

Problems solved by technology

[0003] One of the main difficulties faced in the detection of water tunnel cracks is the complex environment of the detection site. Water tunnels are usually buried deep underground and have the following characteristics: (1) The detection time is limited. Empty, water cut-off maintenance time is determined by the storage capacity of the reservoir in the water-stricken city; (2) The inspection environment is harsh, and there are no lighting facilities in the tunnel. After a period of operation, the tunnel wall is covered with silt and aquatic organisms, and the floor is wet and slippery, making it difficult to walk; (3) The length of the tunnel is super long and the diameter is large, and the length of the water delivery tunnel is generally too long, with many ups and downs, which makes the detection more time-consuming and laborious; (4) The entrance is small, which limits the volume of the water delivery tunnel disease detection equipment. Large-scale equipment inconvenient entry

At present, the manual census method is still used for the detection of water tunnel diseases. The characteristics of the above-mentioned water transportation tunnels determine that the manual census method is time-consuming, laborious, and has poor detection accuracy. It cannot realize digital monitoring of the disease development process, let alone scientific analysis. And research, can not meet the requirements of my country's existing long-distance water tunnel disease detection

[0004] At present, domestic and foreign methods for detecting cracks in water tunnels are mostly based on image processing technology, which identifies and evaluates cracks from the grayscale texture features of cracks. , uneven lighting and severe noise pollution, resulting in ineffective detection of cracks

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