CN-114894814-B - Bridge and culvert detection method, computer and equipment

CN114894814BCN 114894814 BCN114894814 BCN 114894814BCN-114894814-B

Abstract

The invention discloses a bridge and culvert detection method which comprises the steps of obtaining datum point cloud data, obtaining target point cloud data, matching the target point cloud data with the datum point cloud data in the same coordinate system, carrying out overlapping comparison on the target point cloud data and the datum point cloud data in the same coordinate system to obtain difference point cloud data, wherein the difference point cloud data comprises datum difference point cloud data and target difference point cloud data, carrying out analysis processing on the datum difference point cloud data and the target difference point cloud data to generate an analysis result, converting the datum point cloud data into a datum three-dimensional model, converting the target point cloud data into a target three-dimensional model, converting the datum three-dimensional model into a datum VR panorama, and converting the target three-dimensional model into a target VR panorama. Correspondingly, the invention also discloses a computer and equipment related to the method. The invention has the advantages of wide detection coverage, high safety, high detection efficiency and accuracy.

Inventors

Zeng tianyang
LIU XIN
WANG XIANG
WANG LIANGBO
LIU YONGXIANG
WU YOUJUN
Yuan Zechang
LI DONGLONG
DU JINGQIANG
LUO LEI

Assignees

佛山市公路桥梁工程监测站有限公司
佛山市公路桥梁工程监测站有限公司

Dates

Publication Date: 20260421
Application Date: 20220324
Priority Date: 20220324

Claims (9)

1. A bridge and culvert detection method, comprising: Acquiring datum point cloud data; Acquiring target point cloud data, wherein the target point cloud data and the reference point cloud data comprise three-dimensional coordinates and laser reflection intensity; Matching the target point cloud data with the reference point cloud data in the same coordinate system; The method comprises the steps of carrying out overlapping comparison on target point cloud data and reference point cloud data in the same coordinate system, and eliminating the target point cloud data and the reference point cloud data with the same three-dimensional coordinates and laser reflection intensity to obtain difference point cloud data, wherein the difference point cloud data comprises reference difference point cloud data and target difference point cloud data; Analyzing the reference differential point cloud data and the target differential point cloud data to generate an analysis result; converting the datum point cloud data into a datum three-dimensional model; converting the target point cloud data into a target three-dimensional model; Converting the reference three-dimensional model into a reference VR panorama; Converting the target three-dimensional model into a target VR panorama; The step of analyzing the reference differential point cloud data and the target differential point cloud data to generate an analysis result comprises the steps of judging whether three-dimensional coordinates in the target differential point cloud data are consistent with three-dimensional coordinates in the reference differential point cloud data, judging that a target area is defective when judging that the three-dimensional coordinates in the target differential point cloud data are not consistent with the three-dimensional coordinates in the reference differential point cloud data, judging that whether laser reflection intensity in the target differential point cloud data is smaller than preset laser reflection intensity when judging that the laser reflection intensity in the target differential point cloud data is smaller than the preset laser reflection intensity, judging that water leakage occurs in the target area when the laser reflection intensity in the target differential point cloud data is larger than or equal to the preset laser reflection intensity, and judging that the target area is normal.
2. The bridge and culvert detection method of claim 1, wherein the step of analyzing the reference differential point cloud data and the target differential point cloud data to generate an analysis result further includes: when a defect occurs in a target area, obtaining defect point cloud data in the target differential point cloud data; calculating the three-dimensional size of the defect according to the defect point cloud data; and comparing the three-dimensional size of the defect with a preset size to evaluate the defect grade.
3. The bridge and culvert detection method of claim 2 wherein the step of analyzing the reference differential point cloud data and the target differential point cloud data to generate an analysis result further includes: when the target area has water leakage, acquiring the data of the leakage point cloud in the data of the target differential point cloud; Calculating leakage area according to the leakage point cloud data; comparing the leakage area with a preset area to evaluate the leakage grade.
4. The bridge and culvert detection method of claim 3 wherein the step of analyzing the baseline differential point cloud data and target differential point cloud data to generate analysis results further includes: and generating an analysis report according to the judging result and the defect three-dimensional size, the defect grade, the leakage area and the leakage grade.
5. The bridge and culvert detection method of claim 2 wherein the step of calculating a defect three-dimensional size from the defect point cloud data includes: acquiring a point cloud reference plane of the defect point cloud data; dividing the point cloud reference plane into a plurality of reference plane grids; counting the number of grids of each row of reference surface, and taking the maximum value to obtain a first length value; counting the number of grids of each column of the reference surface, and taking the maximum value to obtain a second length value; generating a plurality of cuboids according to each point cloud in the defect point cloud data and the corresponding reference plane grid; Calculating the height value of each cuboid, and taking the maximum value to obtain a depth value; the volume of each cuboid is calculated and the volumes of all cuboids are summed together to obtain the defect volume.
6. The bridge and culvert detection method of claim 3 wherein the step of calculating a leak area from the leak point cloud data includes: Acquiring a point cloud plane of the leakage point cloud data; Dividing the point cloud plane into a plurality of plane grids; and counting the number of all the plane grids to obtain the leakage area.
7. A computer comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the bridge detection method of any one of claims 1 to 6 when the computer program is executed.
8. A bridge and culvert detection device comprising a detection robot, a wireless remote control device, a VR display device, and the computer of claim 7; The detection robot is respectively connected with the wireless remote control device and the computer in a wireless way, and the VR display device is connected with the computer and used for displaying the reference VR panorama and the target VR panorama; The detection robot comprises an intelligent mobile robot and a three-dimensional laser scanner, wherein the three-dimensional laser scanner is arranged on the intelligent mobile robot and used for scanning bridges and culverts to generate datum point cloud data and target point cloud data.
9. The bridge and culvert detection apparatus of claim 8 wherein the intelligent mobile robot is a biomimetic quadruped robot or an amphibious robot; the intelligent mobile robot comprises a body, a camera, a light source, a control module and a wireless communication module, wherein moving mechanisms are arranged on two sides of the body, the control module and the wireless communication module are arranged in the body, the camera and the light source are arranged on the body, the camera is used for collecting real-time images, the light source is used for illumination, and a display used for displaying the real-time images is arranged on the wireless remote control device; The control module is respectively connected with the moving mechanism, the camera, the light source, the three-dimensional laser scanner and the wireless communication module, and the wireless communication module is respectively connected with the wireless remote control device and the computer in a wireless way.

Description

Bridge and culvert detection method, computer and equipment Technical Field The invention relates to the technical field of bridge and culvert detection, in particular to a bridge and culvert detection method, a computer and equipment. Background The bridge and culvert detection comprises two detection items, namely bridge appearance detection and culvert appearance detection. The bridge appearance detection comprises (1) appearance quality inspection of the bridge, including bridge deck system, upper structure, support, lower structure and other auxiliary facilities inspection, and important inspection of whether cracks appear on the main bearing structure of the concrete, (2) whether honeycomb, pitted surface and rib exposing phenomena exist on the concrete structure, and (3) whether other appearance defects and diseases of the concrete exist, whether the mounting quality of the support meets the requirements, and whether the support has void and abnormal deformation conditions. The traditional bridge detection method adopts a bridge detection vehicle as a detection platform, mainly adopts visual observation, and is assisted with detection tools such as a crack width measuring instrument, a laser range finder, a tape measure, a feeler gauge, a digital camera and the like, so that the bridge is inspected at a short distance. The bridge appearance detection method comprises the steps of (1) checking water passing capability of a culvert, (2) checking whether water inlet and outlet paving, wing walls, slope protection, water retaining walls and the like are complete, whether hole connection is smooth and proper, (3) whether a culvert body wall is water leakage, cracking, deformation or inclination, whether body building mortar falls off, stones are loose, a foundation is scoured and hollowed, (4) whether a culvert top cover plate or a culvert top is cracked, water leakage and deformation downwarping, (5) whether a culvert bottom is silted and blocked, and whether paving of the culvert bottom is complete, (6) whether hole accessories are filled with water, scour and hollow and filling soil is stable, and (7) whether a pavement on the top of the culvert is cracked, sunk and running is safe. The traditional culvert detection method adopts bridge detection vehicles, movable hanging baskets, ladders and the like as detection platforms, mainly adopts visual observation, carries crack width measuring instruments, and detection tools such as a tape measure, a digital camera and the like, and performs close-range inspection on the culvert. From the above, the traditional bridge and culvert detection method is mainly based on manual work, but the inside light of the box chamber of the oversized bridge is insufficient, the height of the box girder is too high, and the conditions of insufficient light, sewage residue, silt accumulation and toxic gas flow exist in the culvert, so that the detection environment is very bad, the detection efficiency is low, the detection risk is high, safety accidents such as gas poisoning and high-altitude falling are easy to occur, and detection staff in dangerous areas cannot enter, the detection range is limited, and the situation of misjudgment, missed judgment and disease is easy to occur. Disclosure of Invention The invention aims to solve the technical problem of providing a bridge and culvert detection method, a computer and equipment, and has the advantages of wide detection coverage, high safety, high detection efficiency and high accuracy. The invention provides a bridge and culvert detection method, which comprises the steps of obtaining datum point cloud data, obtaining target point cloud data, matching the target point cloud data with the datum point cloud data in the same coordinate system, carrying out overlapping comparison on the target point cloud data and the datum point cloud data to obtain difference point cloud data, wherein the difference point cloud data comprises datum difference point cloud data and target difference point cloud data, carrying out analysis processing on the datum difference point cloud data and the target difference point cloud data to generate an analysis result, converting the datum point cloud data into a datum three-dimensional model, converting the target point cloud data into a target three-dimensional model, converting the datum three-dimensional model into a datum VR, and converting the target panoramic three-dimensional model into a target VR. The step of analyzing the reference differential point cloud data and the target differential point cloud data to generate an analysis result includes judging whether three-dimensional coordinates in the target differential point cloud data are consistent with three-dimensional coordinates in the reference differential point cloud data, judging that a target area is defective when judging that the three-dimensional coordinates in the target differential point cloud data are not consistent with the