CN112461136A - Bridge crack detection system - Google Patents
Bridge crack detection system Download PDFInfo
- Publication number
- CN112461136A CN112461136A CN202011246014.3A CN202011246014A CN112461136A CN 112461136 A CN112461136 A CN 112461136A CN 202011246014 A CN202011246014 A CN 202011246014A CN 112461136 A CN112461136 A CN 112461136A
- Authority
- CN
- China
- Prior art keywords
- bridge
- crack
- image
- unmanned aerial
- aerial vehicle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
Abstract
The application discloses bridge crack detecting system. One embodiment of the bridge crack detection system comprises: the unmanned aerial vehicle system, the image acquisition device, data transmission device and data processing device, the image acquisition device sets up in the unmanned aerial vehicle system to carry out image acquisition according to the instruction of unmanned aerial vehicle system or preset procedure to the bottom surface of bridge, data processing device is used for handling the image that the image acquisition device gathered, confirms the cracked information of bridge bottom surface that the image corresponds, data transmission device is used for the data transmission between image acquisition device and the data processing device. The embodiment can conveniently, quickly and safely extract the image of the bridge, and effectively and conveniently detect the crack information of the bridge through the data processing device.
Description
Technical Field
The application relates to the technical field of bridge safety detection, in particular to a bridge crack detection system.
Background
At present, with the development of traffic, more and more bridges are built at places crossing rivers, ravines, and the like. The bridge becomes an important component in a traffic system, and the safety of the bridge has great influence on travel. The bridge has some potential safety hazards under the influence of overload, overrun and bridge aging, so that the bridge needs to be detected relatively. The bridge quality detection method mainly comprises the steps of detecting cracks of a bridge and determining whether potential safety hazards exist in the bridge according to the length, the width and other conditions of the cracks.
In recent years, crack detection on the bottom surface of a bridge is mainly completed by a manual detection method. Specifically, the crack of the bridge bottom is observed remotely through a telescope, and the crack of the bridge is observed closely on a platform built at the bridge bottom. The information of the bridge cracks cannot be accurately detected by long-distance observation of the bridge cracks through the telescope, so that the quality of the bridge cannot be accurately evaluated. The method for building the observation platform at the bottom of the bridge to detect the cracks of the bridge in a short distance has the advantages that the cost of building a special safety frame is high, detection personnel walk on a truss arm of a bridge detection vehicle to detect the cracks of the bottom surface of the bridge, the safety is low, the manual detection speed is low, and the efficiency is low.
Disclosure of Invention
The application aims to provide a bridge crack detection system, and solves the technical problems mentioned in the background technology. The problems of high manual detection cost, low safety and low efficiency in bridge crack detection are solved.
The application provides a bridge crack detecting system, this bridge crack detecting system includes: the unmanned aerial vehicle system, the image acquisition device, data transmission device and data processing device, wherein, above-mentioned image acquisition device sets up in the unmanned aerial vehicle system to carry out image acquisition according to unmanned aerial vehicle system's instruction or preset procedure to the bottom surface of bridge, above-mentioned data processing device is used for handling the image of above-mentioned image acquisition device collection, confirms the cracked information of bridge bottom surface that above-mentioned image corresponds, above-mentioned data transmission device is used for the data transmission between above-mentioned image acquisition device and the above-mentioned data processing device.
In some examples, the unmanned aerial vehicle system includes a positioning device, and the positioning device determines a boundary of the bridge to be measured according to an interval between the bridges, and determines a length and a width of the bridge to be measured according to the boundary of the bridge to be measured.
In some examples, the positioning device retrieves a pre-stored bridge shearing force data information table, and determines the crack detection area of the bridge to be detected according to the bridge shearing force data information table and the length and width of the bridge to be detected, where the bridge shearing force data information table is a list of three areas with different lengths and widths where the bridge to be detected bears the largest shearing force, and the three areas with the largest shearing force are determined to be the crack detection area of the bridge to be detected.
In some examples, the positioning device measures the distance between the unmanned aerial vehicle where the unmanned aerial vehicle system is located and the bottom surface of the bridge to be measured according to the built-in range finder, and adjusts the distance between the unmanned aerial vehicle and the bottom surface of the bridge to be measured.
In some examples, the image acquisition device acquires an image of the crack detection area of the bridge to be detected.
In some examples, the data processing apparatus includes an image processing unit that performs filtering and denoising processing on an image to be processed to reduce noise influence of the image to be processed, a feature extraction unit that performs image segmentation on information of a crack extracted from the image to be processed to generate a crack feature map, and a crack information determination unit that acquires information of the crack from the crack feature map to determine a crack length and a crack width.
In some examples, the feature extraction unit segments an image region larger than a set threshold value based on different gray levels of each pixel point in the image to be processed, so as to generate a crack feature map.
In some examples, the crack information determining unit determines an outline region formed by discontinuous pixel points in the crack feature map as a crack region, and determines the length and the bandwidth of the crack of the bridge to be detected according to the crack region.
The application provides a bridge crack detecting system carries out image acquisition to the bridge bottom surface through the image acquisition device who installs at unmanned aerial vehicle system, and data processing device handles the image of gathering, determines the cracked information of bridge bottom surface that the image corresponds. According to the scheme, the positioning device of the unmanned aerial vehicle system positions the unmanned aerial vehicle at a set distance from the bottom surface of the bridge, and extracts the image of the predetermined area at a predetermined angle, so that the quality of the acquired image is ensured; the data processing device judges the crack area based on the gray threshold value, and realizes accurate recognition of the crack. Compared with the existing detection method or system, the method and the system have the advantages of low cost, high detection efficiency, reduction of labor intensity of detection personnel and improvement of detection quality.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a schematic structural diagram of an embodiment of a bridge crack detection system according to the present application;
FIG. 2 is a schematic diagram illustrating distribution of shearing force applied to a box girder of a bridge in a vehicle passing process according to an embodiment of the bridge crack detection system.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Referring to fig. 1, fig. 1 shows a schematic structural diagram of an embodiment of a bridge crack detection system to which the present application may be applied.
As shown in fig. 1, the bridge crack detection system includes an unmanned aerial vehicle system 1, an image acquisition device 2, a data transmission device 3 and a data processing device 4, wherein the image acquisition device 2 is arranged on the unmanned aerial vehicle system 1, and performs image acquisition on the bottom surface of the bridge according to an instruction of the unmanned aerial vehicle system 1 or a preset program, the data processing device 4 is used for processing an image acquired by the image acquisition device 2, determining information of a crack on the bottom surface of the bridge corresponding to the image, and the data transmission device 3 is used for data transmission between the image acquisition device 2 and the data processing device 4.
In this embodiment, unmanned aerial vehicle is electronic six rotor unmanned aerial vehicle among the above-mentioned unmanned aerial vehicle system 1, can keep the fuselage stable in carrying out bridge crack and detecting, reduces to rock or shake the noise interference of the image of gathering that causes. Simultaneously, because of the needs that detect bridge ground, above-mentioned image acquisition device 2 installs on the upper portion of unmanned aerial vehicle fuselage for when unmanned aerial vehicle suspends in the detection area, openly gather the image of bridge bottom surface. The image capturing device 2 may be a video or image capturing device such as a camera or a video camera. In the embodiment, the multispectral camera is adopted to collect images of the bridge cracks, and a plurality of spectral line images can be collected simultaneously.
In this embodiment, the data processing device 4 performs data processing on the acquired image, determines whether a bridge has a crack, the length and the width of the crack, the distribution of the crack, and other bridge crack information according to the analysis of the image, and the inspector gives the bridge crack information to draw a conclusion on the safety of the crack of the bridge structure, including the existing bearing capacity of the structure and the reliability analysis of the normal use.
In this embodiment, the data transmission device 3 realizes data communication between the data processing device 4 and the image acquisition device 2, and in a specific application, the data transmission device 3 further realizes information transmission between the unmanned aerial vehicle and ground equipment or a data center. The state information of the unmanned aerial vehicle system 1 and information such as obstacles in the limit contact area of the unmanned aerial vehicle system are transmitted to the ground equipment or the data center in real time, and information such as instructions of the ground equipment or the data center is sent to the unmanned aerial vehicle system 1, for example, instruction information such as adjusting the measurement range and the height of the body during measurement.
In some optional implementation manners of this embodiment, the unmanned aerial vehicle system 1 includes a positioning device, and the positioning device determines the boundary of the bridge to be measured according to the interval between the bridges, and determines the length and the width of the bridge to be measured according to the boundary of the bridge to be measured.
Specifically, as the spacing gaps are reserved between the box girders forming the bridge floor, the positioning device can send infrared information or sound wave information to the bridge floor through the infrared sensor or the sound wave sensor, determine the boundary of the box girders according to the returned infrared information or sound wave information between the spacing gaps and the complete bridge floor, determine the boundary of the box girders as the boundary of the bridge to be detected, and determine the length and the width of the bridge to be detected according to the boundary of the bridge to be detected.
In some optional implementation manners of this embodiment, the positioning device retrieves a pre-stored bridge shearing force data information table, and determines the crack detection area of the bridge to be detected according to the bridge shearing force data information table and the length and width of the bridge to be detected, where the bridge shearing force data information table is a list of three areas with different lengths and widths where the shearing force borne by the bridge is the largest, and the three areas with the largest shearing force borne by the bridge to be detected are determined as the crack detection area of the bridge to be detected.
In this embodiment, the three regions, in which the shear force is the largest when each box girder runs, can be determined by analyzing the stress of the box girders with different lengths, widths and thicknesses in the vehicle passing process in advance through mechanical analysis. As shown in fig. 2, fig. 2 shows a schematic diagram of the distribution of the shear force applied to the box girder of the bridge when a vehicle passes through. As can be seen, the three regions where each box girder experiences the greatest shear force during operation are the middle region centered on the center line and the region approximately one fifth. The image acquisition device 2 is aligned to the area during crack detection, and the image of the area is acquired in the front side. The method comprises the steps of compiling a list of shear forces borne by bridges of different specifications (different lengths and widths) in the driving direction in advance. And determining three areas with the largest shearing force borne by the bridge as crack detection areas according to the list and the information of the length and the width of the bridge determined in the previous step.
In some optional implementation manners of this embodiment, the positioning device measures the distance between the unmanned aerial vehicle where the unmanned aerial vehicle system 1 is located and the bottom surface of the bridge to be measured according to the built-in distance meter, and adjusts the distance between the unmanned aerial vehicle and the bottom surface of the bridge to be measured.
Specifically, above-mentioned positioner includes a plurality of sensors, and the sensor is arranged at unmanned aerial vehicle fuselage middle part, sets up the flight environment that can the limit contact area that the radius is 1m is used for controlling unmanned aerial vehicle's flight state and monitoring range, prevents the collision of unmanned aerial vehicle and barrier. The sensors comprise a distance measuring sensor, an inclination angle sensor and an acceleration sensor, and the sensors send data to the fuselage stabilizer so as to adjust the position of the fuselage. Here, utilize the distancer to measure the distance between unmanned aerial vehicle and the bridge bottom surface that awaits measuring, the distance between its purpose makes image acquisition device 2's camera and the bridge floor is predetermined distance for the series of pictures of gathering have the same distance characteristic. Above-mentioned distancer is connected with the fuselage stabilizer, based on the distance that the distancer was measured can be timely adjustment unmanned aerial vehicle and the distance of the above-mentioned bridge bottom surface that awaits measuring.
In some optional implementation manners of this embodiment, the image acquisition device 2 acquires an image of the crack detection area of the bridge to be detected. In the crack detection area of each bridge, the unmanned aerial vehicle suspends in the area and carries out image acquisition under.
In some optional implementations of the embodiment, the data processing apparatus 4 includes an image processing unit, a feature extraction unit, and a crack information determination unit, the image processing unit performs filtering and denoising processing on an image to be processed to reduce noise influence of the image to be processed, the feature extraction unit extracts information of cracks from the image to be processed to perform image segmentation to generate a crack feature map, and the crack information determination unit acquires information of cracks from the crack feature map to determine a length and a width of the cracks.
In some optional implementation manners of this embodiment, the feature extraction unit segments an image region larger than a set threshold value based on different gray levels of each pixel point in the image to be processed, and generates a crack feature map.
In some optional implementation manners of this embodiment, the crack information determining unit determines, as a crack region, an outline region formed by discontinuous pixel points in the crack feature map, and determines the length and the bandwidth of the crack of the bridge to be measured according to the crack region.
In this embodiment, the image capturing device 2 may be interfered by noise due to gradual noise or uneven illumination conditions during image capturing or imaging, and oil stains on the bridge floor or other irregular objects other than concrete. The image processing unit carries out filtering processing on the image, weakens the influence of noise and improves the quality of the image. Specifically, a median filtering method can be adopted to perform filtering processing on the image to be processed, the median filtering can well retain the image edge, the image contour is clear, and a good smoothing effect can be obtained.
In this embodiment, the characteristic extraction unit may determine the crack region through gray scale comparison, specifically, select a gray scale threshold in advance, segment the pixel characteristic point in the image whose gray scale value is greater than the gray scale threshold, and generate a crack characteristic map.
And determining the equal-gray connecting line area as a crack boundary area according to the pixel gray value of the characteristic point by the pixel characteristic point which is obtained by segmenting the crack information from the crack characteristic diagram. Because burrs, bulges and the like exist on the edge of the crack, an isolated area with large variation of the gray value of the pixel point with small area is formed. In some applications, this region may be determined as a fracture boundary region. The data processing device 4 may determine the length and the width of the crack of the bridge to be measured through the crack region.
In the bridge crack detection system in the embodiment, the unmanned aerial vehicle system is used for collecting the image of the bottom surface of the bridge, so that the crack information of the bridge can be conveniently, quickly and safely extracted; the positioning device arranged on the unmanned aerial vehicle system can position the unmanned aerial vehicle in a designated area of the bottom surface of the bridge, so that the image acquisition device can be over against a crack detection area, and meanwhile, the distance between the body of the unmanned aerial vehicle and the bottom surface of the bridge is kept to be a preset value by the range finder, so that the consistency of the resolution of the acquired image is kept, and the image quality is ensured; the data processing device segments the crack boundaries by acquiring different gray values of pixel points of the images, determines the length and the width of the crack based on the crack boundaries, and can effectively and conveniently detect the crack information of the bridge.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (8)
1. A bridge crack detection system, comprising: unmanned aerial vehicle system, image acquisition device, data transmission device and data processing device, wherein, image acquisition device set up in on the unmanned aerial vehicle system, and according to unmanned aerial vehicle system's instruction or preset procedure carry out image acquisition to the bottom surface of bridge, data processing device is used for right the image that image acquisition device gathered is handled, confirms the cracked information of bridge bottom surface that the image corresponds, data transmission device be used for image acquisition device with data transmission between the data processing device.
2. The bridge crack detection system of claim 1, wherein the unmanned aerial vehicle system comprises a positioning device, the positioning device determines the boundary of the bridge to be detected according to the interval between the bridges, and determines the length and the width of the bridge to be detected according to the boundary of the bridge to be detected.
3. The bridge crack detection system of claim 2, wherein the positioning device retrieves a pre-stored bridge shear force data information table, and determines the crack detection area of the bridge to be detected according to the bridge shear force data information table and the length and width of the bridge to be detected, wherein the bridge shear force data information table is a list of three areas with the largest shear force borne by bridges with different lengths and widths, and the three areas with the largest shear force borne by the bridge to be detected are determined as the crack detection area of the bridge to be detected.
4. The bridge crack detection system of claim 3, wherein the positioning device measures the distance between the unmanned aerial vehicle where the unmanned aerial vehicle system is located and the bottom surface of the bridge to be detected according to a built-in range finder, and adjusts the distance between the unmanned aerial vehicle and the bottom surface of the bridge to be detected.
5. The bridge crack detection system of claim 4, wherein the image acquisition device acquires the image of the crack detection area of the bridge to be detected at a preset distance from the bottom surface of the bridge to be detected.
6. The bridge crack detection system of claim 5, wherein the data processing device comprises an image processing unit, a feature extraction unit and a crack information determination unit, the image processing unit performs filtering and denoising processing on the image to be processed to reduce the noise influence of the image to be processed, the feature extraction unit extracts crack information from the image to be processed for image segmentation to generate a crack feature map, and the crack information determination unit acquires crack information from the crack feature map to determine the length and width of the crack.
7. The bridge crack detection system of claim 6, wherein the feature extraction unit segments an image region larger than a set threshold value based on a difference in gray level of each pixel point in the image to be processed to generate a crack feature map.
8. The bridge crack detection system of claim 7, wherein the crack information determination unit determines the crack region according to an outline region formed by discontinuous pixel points in the crack feature map, and determines the length and the bandwidth of the crack of the bridge to be detected according to the crack region.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011246014.3A CN112461136A (en) | 2020-11-10 | 2020-11-10 | Bridge crack detection system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011246014.3A CN112461136A (en) | 2020-11-10 | 2020-11-10 | Bridge crack detection system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112461136A true CN112461136A (en) | 2021-03-09 |
Family
ID=74825943
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011246014.3A Withdrawn CN112461136A (en) | 2020-11-10 | 2020-11-10 | Bridge crack detection system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112461136A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114049356A (en) * | 2022-01-17 | 2022-02-15 | 湖南大学 | Method, device and system for detecting structure apparent crack |
-
2020
- 2020-11-10 CN CN202011246014.3A patent/CN112461136A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114049356A (en) * | 2022-01-17 | 2022-02-15 | 湖南大学 | Method, device and system for detecting structure apparent crack |
| CN114049356B (en) * | 2022-01-17 | 2022-04-12 | 湖南大学 | Method, device and system for detecting structure apparent crack |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jiang et al. | Real‐time crack assessment using deep neural networks with wall‐climbing unmanned aerial system | |
| CN108230344B (en) | Automatic identification method for tunnel water leakage diseases | |
| Li et al. | Automatic bridge crack detection using Unmanned aerial vehicle and Faster R-CNN | |
| CN103778681B (en) | A kind of vehicle-mounted highway cruising inspection system and data acquisition and disposal route | |
| Ribeiro et al. | Remote inspection of RC structures using unmanned aerial vehicles and heuristic image processing | |
| KR101032160B1 (en) | System and method for road visibility measurement using camera | |
| CN109682316A (en) | Distress in concrete recognition methods and system based on unmanned plane imaging | |
| WO2020194539A1 (en) | Structure displacement measurement device | |
| KR20200007165A (en) | Pot-hole detection system using vision sensor mounted on unmanned air vehicle, and method for the same | |
| CN103438836A (en) | Device, system and method for measuring bending angle of bent piece | |
| Peng et al. | The feasibility assessment study of bridge crack width recognition in images based on special inspection UAV | |
| CN109556521B (en) | Railway steel rail displacement detection method based on unmanned aerial vehicle | |
| CN112461136A (en) | Bridge crack detection system | |
| CN110674732B (en) | Asphalt pavement rut three-dimensional size automatic detection and positioning method integrating multivariate data | |
| KR102427308B1 (en) | Present serviceability evaluating system for road pavement using spectral data, and method for the same | |
| JP3626461B2 (en) | Ground surface inspection method, ground surface inspection system, and ground surface inspection device | |
| JP3399656B2 (en) | Pavement patrol device | |
| Ribeiro et al. | Remote inspection and monitoring of civil engineering structures based on unmanned aerial vehicles | |
| CN113222907B (en) | Detection robot based on curved rail | |
| KR102210571B1 (en) | Bridge and tunnel safety diagnosis remote monitoring alarm method using GPS coordinates and mobile communication system | |
| KR102310900B1 (en) | Diagnostic apparatus for facilities of power transmission using unmaned aerial vehicle and method thereof | |
| JP4697761B2 (en) | Queue detection method and queue detection apparatus | |
| JP7246046B2 (en) | Ground deformation evaluation method | |
| KR102317104B1 (en) | Method for improving accuracy of foreign objects detection by setting detection exclusion zone | |
| WO2016084308A1 (en) | Image transformation apparatus and image transformation method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20210309 |