CN115078382A - Bridge crack monitoring system based on video image - Google Patents
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Abstract
The invention discloses a bridge crack monitoring system based on video images, which comprises a front-end system and a back-end system, wherein the front-end system comprises: the system comprises a camera, a micro-control system, a power supply module and a wireless transmission module, wherein a front-end system is integrally installed on the surface of a measured structure, and a rear-end system comprises: the crack precision calculation system comprises a data storage module and a data display module, wherein the back-end system is used for storing and displaying crack information sent by the front-end system, data are stored in a cloud server and can be displayed on a PC (personal computer) end or a mobile phone app, the front-end system and the back-end system are used for accurately calculating cracks, and the accurate calculation of the cracks is composed of five parts, namely image acquisition, image preprocessing, image segmentation, pixel calibration and width measurement. The device is wireless, low in power consumption and convenient to install, does not damage a structure, is internally provided with a crack edge algorithm, and can realize full-automatic high-precision measurement, an automatic dormancy and awakening function and a crack triggering alarm function.
Description
Technical Field
The invention relates to the technical field of bridge health monitoring, in particular to a bridge crack monitoring system based on video images.
Background
In recent years, the traffic industry of China is rapidly developed, a large number of bridges are built all over the country, however, in the process of bridge construction and use, potential safety hazards and even collapse accidents of the bridges frequently occur due to the crack problem of a concrete structure, the crack is the most common disease in the concrete structure, the appearance of the crack not only damages the attractiveness of the bridge and reduces the stress area of a cross section, but also influences the anti-permeability performance of the structure, causes the infiltration of moisture and harmful substances, induces the corrosion of reinforcing steel bars or accelerates the natural aging of concrete, thereby damaging the bearing capacity of the bridge structure and generating adverse influence on the safety performance of the bridge. Therefore, important detection and tracking monitoring are necessary for the bridge cracks.
The traditional concrete crack detection method is manual detection, which mainly means that a detector directly observes cracks on the surface of a structure by naked eyes or observes cracks on the surface of the structure by auxiliary equipment such as a telescope, a bridge inspection vehicle, a climbing vehicle and the like, although the manual detection method is convenient and flexible to operate, however, the method also has the problems of high difficulty in detecting high-altitude components, easy fatigue of detection personnel, high subjectivity, serious phenomena of missed detection and false detection and the like, the traditional concrete crack monitoring technology mainly relies on a vibrating wire crack meter or a fiber grating crack meter, the sensor is arranged on the surface of the concrete structure, when the structure is deformed, the change condition of the crack is measured by capturing the output signal of the sensor, wherein, the crack is measured by the vibrating wire crack gauge, the sensing range of the sensor is limited and is only 10-20 cm, therefore, the device is generally installed at the position of a known crack, and the time and the position of the new crack cannot be sensed; the crack meter is influenced by the fracture strain of the steel wire and the optical fiber, the measuring range is limited, the general measuring range is within 6000 micro strain, and the crack exceeds 0.2
The crack will be broken after mm, the whole process of crack development is difficult to track, and the fiber grating crack meter for measuring the crack has the defects of low sensitivity, difficulty in effectively separating and extracting crack information in complex environments with severe temperature change, frequent vibration and the like, and less application in practical engineering.
In addition, with the progress of scientific technology, some novel bridge crack monitoring technologies are also created, such as conductive coating crack monitoring, a conductive film is formed by coating flexible conductive coating on the surface of a structure, when the conductive film is stretched, the contact surface between conductive particles is reduced, the resistance is changed, and then the structure crack monitoring is realized by measuring the resistance change condition; smart grid crack monitoring, which uses enameled copper wires as sensing and communication materials, forms grid coordinates on the surface of a structure, and captures the development condition of cracks through the fracture of enameled wires in a monitoring area when the cracks appear on the structure; the two methods have the defects that a structure is damaged, the early-stage investment cost is high, the construction difficulty is high, an input front-end sensor cannot be recycled, a bridge crack monitoring method based on machine vision becomes a research hotspot in recent years, the length and the width of a crack are identified by a computer through capturing an image of a measured object, and the method has the characteristics of convenience in installation, no damage to the structure, reusability of equipment and the like.
Therefore, aiming at the technical problem of real-time visual monitoring of cracks in the structural characterization diseases, the project is intended to develop a structural crack visual monitoring system, an optical imaging system and a photoelectric sensing system are used for recording images of the widest position of the crack at regular time, and then special software is used for processing digitized images, so that useful information of the digitized images of the crack can be obtained, and the width of the detected crack can be calculated by using an algorithm.
Disclosure of Invention
Based on the technical problems in the background art, the invention provides a bridge crack monitoring system based on video images.
The invention provides a bridge crack monitoring system based on video images, which comprises a front-end system and a back-end system, wherein the front-end system comprises: the system comprises a camera, a micro-control system, a power supply module and a wireless transmission module, wherein a front-end system is integrally installed on the surface of a measured structure, and a rear-end system comprises: the crack precision calculation system comprises a data storage module and a data display module, wherein the back-end system is used for storing and displaying crack information sent by the front-end system, data are stored in a cloud server and can be displayed on a PC (personal computer) end or a mobile phone app, the front-end system and the back-end system are used for accurately calculating cracks, and the accurate calculation of the cracks is composed of five parts, namely image acquisition, image preprocessing, image segmentation, pixel calibration and width measurement.
Preferably, the camera is a low-power-consumption camera module, and a photographing protective cover and a light supplement lamp are arranged on the camera in an auxiliary mode.
Preferably, the micro control system is used for sending a photographing instruction at regular time, processing and analyzing the acquired image, and sending out the processed crack information.
Preferably, the power supply module is powered by a lithium battery.
Preferably, the wireless transmission module adopts an NB-IoT communication module.
Preferably, the image acquisition: the lighting equipment is used for supplementing light, and the high-definition camera lens module is used for directly shooting and forming a digital image;
image preprocessing: the image on the lowest level is processed, the processed image is a high-brightness image, the content of the preprocessing mainly comprises the procedures of image graying, image enhancement, median filtering, image binarization and the like, the preprocessing can reduce the information content of the original image, because the common image has redundant information, and the preprocessing can also achieve the purpose of improving the image quality by inhibiting the sudden deformation of the image or enhancing some structural features and the like;
extracting crack edges: the method comprises the following steps of extracting the edges of cracks in a box girder and cracks at the inner cross section of a tunnel structure by adopting an LoG algorithm, wherein the extracted crack edges can be more accurate by the LoG algorithm, the LoG algorithm is jointly proposed by Marr and Hildreth, the LoG algorithm is formed by combining Gaussian filtering and Laplace edge detection, the LoG algorithm is called as a Laplace Gaussian algorithm, and the LoG algorithm has the basic principle that: firstly, smoothing filtering processing is carried out on an image by a Gaussian two-dimensional low-pass filter in a certain range, and then the edge of the image is detected by using a Laplace difference operator;
pixel calibration: the digital image processing technology is applied to the measurement of the crack width, pixels of a camera need to be calibrated, namely the actual width represented by each pixel in an image is calibrated, the unit of a calibration index is mm/pix, in the method for calibrating a plurality of pixels, the requirements of the measurement precision and the measurement efficiency of a system are considered, the pixel calibration is realized by adopting a micro-distance method, the distance between a camera lens and the surface of a crack structure is fixed through a micro-distance instrument protection device, and the size scale represented by a single pixel point of the image is calculated by reading the focal length parameter of a lens module;
calculation of crack width: and distinguishing the upper edge and the lower edge of the crack based on the crack boundary identification result, respectively selecting each point of the upper edge, and calculating the width of the target crack by adopting a minimum distance method.
Preferably, the flow of the front-end system and the back-end system is as follows:
s1, fixing the sensor above the structure to be measured, and aligning the camera with the target crack;
s2, sending a photographing instruction by the front-end micro control system at regular time, and continuously photographing the structure by the camera;
s3, the micro control system processes the image, identifies the crack width, and sends the crack width and the crack picture to a remote server through a wireless transmission module;
s4, after the crack image is captured and transmitted, the system automatically enters a dormant state, and automatically wakes up to repeat work when the next set shooting time comes;
and S5, finally, displaying data on the PC side or the mobile phone app.
In the invention, the bridge crack monitoring system based on the video image has the following beneficial effects:
1. the traditional concrete crack monitoring technology cannot give consideration to both crack monitoring precision and measurement range, the invention utilizes Sobel algorithm to remove shadow and extract contour, self-adaptive dynamic filtering to inhibit noise, ROI selects and locks a crack area, a Yolo network quickly identifies cracks and frame difference monitors crack change and other front-edge algorithms are added, more than 30 cracks can be identified simultaneously, the measurement precision can reach 0.02mm, and the crack monitoring efficiency is greatly increased.
2. The conventional image crack recognition device needs to transmit a crack picture to the back end and then perform operation recognition by an image processing server. According to the invention, whether the crack condition is abnormal or not is detected through the algorithm deployed at the edge end, the crack condition can be monitored at the terminal, the condition and the picture are reported to the server only when the abnormal condition occurs, and the detail diagnosis is carried out through strong calculation power of the cloud or manual work, so that the huge flow of picture transmission is saved, and the requirement of an image processing server is reduced.
3. According to taking two pictures every day, the machine runs for 1 minute every time and calculates, the average current is about 200uA, the service life of 10 years can be prolonged when a high-capacity battery (20000mAH) is used, the replacement is convenient, and the long-term crack observation requirement of bridge health monitoring is met.
4. The power supply module and the wireless transmission module are added, so that the long-term online monitoring function is realized; meanwhile, through the development of a front-end micro control system, a related algorithm based on crack image recognition can be integrated into the system, and result data such as crack width and the like are directly transmitted back to the rear end by means of edge calculation, so that the real-time observation of crack change trend and abnormal alarm are realized.
According to the crack monitoring method, the auxiliary support is arranged on the surface of the structure, the micro camera is fixed on the support, the MCU (micro processing unit) sends a shooting instruction at regular time to shoot a target crack, the crack width is obtained through analysis of a preprocessing algorithm, and the crack width and a photo are sent to the cloud platform, so that the crack monitoring method can be realized: the device is wireless, has low power consumption and convenient installation, does not damage a structure, is internally provided with a crack edge algorithm, and can realize full-automatic high-precision measurement, automatic dormancy and awakening functions and a crack triggering alarm function.
Drawings
FIG. 1 is a block diagram of a bridge crack monitoring system based on video images according to the present invention;
FIG. 2 is a schematic diagram of the precise calculation of the crack of the bridge crack monitoring system based on video images according to the present invention;
FIG. 3 is a schematic diagram of a minimum distance method crack width calculation of the bridge crack monitoring system based on video images;
fig. 4 is a back-end system software diagram of the bridge crack monitoring system based on video images.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1-4, a bridge crack monitoring system based on video images includes a front-end system and a back-end system, the front-end system is composed of: the system comprises a camera, a micro-control system, a power supply module and a wireless transmission module, wherein a front-end system is integrally installed on the surface of a measured structure, and a rear-end system comprises: the crack precision calculation system comprises a data storage module and a data display module, wherein the back-end system is used for storing and displaying crack information sent by the front-end system, data are stored in a cloud server and can be displayed on a PC (personal computer) end or a mobile phone app, the front-end system and the back-end system are used for accurately calculating cracks, and the accurate calculation of the cracks is composed of five parts, namely image acquisition, image preprocessing, image segmentation, pixel calibration and width measurement.
In the invention, the camera is a low-power-consumption camera module, and a photographing protective cover and a light supplement lamp are assisted on the camera.
In the invention, the micro control system is used for sending out a photographing instruction at regular time, processing and analyzing the acquired image and sending out the processed crack information.
In the invention, the power supply module adopts a lithium battery for power supply.
In the invention, the wireless transmission module adopts an NB-IoT communication module.
In the invention, the image acquisition: the lighting equipment is used for supplementing light, and the high-definition camera lens module is used for directly shooting and forming a digital image;
image preprocessing: the image on the lowest level is processed, the processed image is a high-brightness image, the content of the preprocessing mainly comprises the procedures of image graying, image enhancement, median filtering, image binarization and the like, the preprocessing can reduce the information content of the original image, because the common image has redundant information, and the preprocessing can also achieve the purpose of improving the image quality by inhibiting the sudden deformation of the image or enhancing some structural features and the like;
extracting crack edges: the method comprises the following steps of extracting the edges of cracks in a box girder and cracks at the inner cross section of a tunnel structure by adopting an LoG algorithm, wherein the extracted crack edges can be more accurate by the LoG algorithm, the LoG algorithm is jointly proposed by Marr and Hildreth, the LoG algorithm is formed by combining Gaussian filtering and Laplace edge detection, the LoG algorithm is called as a Laplace Gaussian algorithm, and the LoG algorithm has the basic principle that: firstly, smoothing filtering processing is carried out on an image by a Gaussian two-dimensional low-pass filter in a certain range, and then the edge of the image is detected by using a Laplace difference operator;
pixel calibration: the digital image processing technology is applied to the measurement of the crack width, pixels of a camera need to be calibrated, namely the actual width represented by each pixel in an image is calibrated, the unit of a calibration index is mm/pix, in the method for calibrating a plurality of pixels, the requirements of the measurement precision and the measurement efficiency of a system are considered, the pixel calibration is realized by adopting a micro-distance method, the distance between a camera lens and the surface of a crack structure is fixed through a micro-distance instrument protection device, and the size scale represented by a single pixel point of the image is calculated by reading the focal length parameter of a lens module;
calculation of crack width: and distinguishing the upper edge and the lower edge of the crack based on the crack boundary identification result, respectively selecting each point of the upper edge, and calculating the width of the target crack by adopting a minimum distance method.
In the invention, the working flows of the front-end system and the back-end system are as follows:
s1, fixing the sensor above the structure to be measured, and aligning the camera with the target crack;
s2, sending a photographing instruction by the front-end micro-control system at regular time, and continuously photographing the structure by the camera;
s3, the micro control system processes the image, identifies the crack width, and sends the crack width and the crack picture to a remote server through a wireless transmission module;
s4, after the crack image is captured and transmitted, the system automatically enters a dormant state, and automatically wakes up to repeat work when the next set shooting time comes;
and S5, finally, displaying data on the PC side or the mobile phone app.
The invention comprises the following steps: fixing a sensor above a measured structure, and aligning a camera to a target crack; sending a photographing instruction by a front-end micro control system at regular time, and continuously photographing the structure by a camera; then the micro control system processes the image, identifies the crack width and sends the crack width and the crack picture to a remote server through a wireless transmission module; after the crack image is captured and transmitted, the system automatically enters a dormant state, and automatically wakes up to perform repeated work when the next set shooting time comes; and finally, displaying data on a PC (personal computer) end or a mobile phone app.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (7)
1. Bridge crack monitoring system based on video image, its characterized in that includes front end system and back-end system, front end system by: the system comprises a camera, a micro-control system, a power supply module and a wireless transmission module, wherein a front-end system is integrally installed on the surface of a measured structure, and a rear-end system comprises: the crack precision calculation system comprises a data storage module and a data display module, wherein the back-end system is used for storing and displaying crack information sent by the front-end system, data are stored in a cloud server and can be displayed on a PC (personal computer) end or a mobile phone app, the front-end system and the back-end system are used for accurately calculating cracks, and the accurate calculation of the cracks is composed of five parts, namely image acquisition, image preprocessing, image segmentation, pixel calibration and width measurement.
2. The bridge crack monitoring system based on video images of claim 1, wherein the camera is a low power consumption camera module, and a photographing protection cover and a light supplement lamp are assisted on the camera.
3. The bridge crack monitoring system based on video images as claimed in claim 1, wherein the micro control system is configured to issue a photographing instruction at regular time, process and analyze the acquired image, and send out the crack information obtained after processing.
4. The bridge crack monitoring system based on video images of claim 1, wherein the power module is powered by a lithium battery.
5. The video-image-based bridge crack monitoring system of claim 1, wherein the wireless transmission module employs an NB-IoT communication module.
6. The video image-based bridge fracture monitoring system of claim 1, wherein the image acquisition: the lighting equipment is used for supplementing light, and the high-definition camera lens module is used for directly shooting and forming a digital image;
image preprocessing: the image on the lowest level is processed, the processed image is a high-brightness image, the content of the preprocessing mainly comprises the procedures of image graying, image enhancement, median filtering, image binarization and the like, the preprocessing can reduce the information content of the original image, because the common image has redundant information, and the preprocessing can also achieve the purpose of improving the image quality by inhibiting the sudden deformation of the image or enhancing some structural features and the like;
extracting crack edges: the method comprises the following steps of extracting the edges of cracks in box beams and cracks at the cross section inside a tunnel structure by adopting an LoG algorithm, wherein the extracted crack edges can be more accurate by the LoG algorithm, the LoG algorithm is jointly provided by Marr and Hildreth, the LoG algorithm is formed by combining Gaussian filtering and Laplace edge detection, the LoG algorithm is called Laplace Gaussian algorithm, and the basic principle of the LoG algorithm is as follows: firstly, smoothing filtering processing is carried out on an image by a Gaussian two-dimensional low-pass filter in a certain range, and then the edge of the image is detected by using a Laplace difference operator;
pixel calibration: the digital image processing technology is applied to the measurement of the crack width, pixels of a camera need to be calibrated, namely the actual width represented by each pixel in an image is calibrated, the unit of a calibration index is mm/pix, in the method for calibrating a plurality of pixels, the requirements of the measurement precision and the measurement efficiency of a system are considered, the pixel calibration is realized by adopting a micro-distance method, the distance between a camera lens and the surface of a crack structure is fixed through a micro-distance instrument protection device, and the size scale represented by a single pixel point of the image is calculated by reading the focal length parameter of a lens module;
calculation of crack width: and distinguishing the upper edge and the lower edge of the crack based on the crack boundary identification result, respectively selecting each point of the upper edge, and calculating the width of the target crack by adopting a minimum distance method.
7. The video-image-based bridge crack monitoring system of claim 1, wherein the front-end system and the back-end system work as follows:
s1, fixing the sensor above the structure to be measured, and aligning the camera with the target crack;
s2, sending a photographing instruction by the front-end micro control system at regular time, and continuously photographing the structure by the camera;
s3, the micro control system processes the image, identifies the crack width, and sends the crack width and the crack picture to a remote server through a wireless transmission module;
s4, after the crack image is captured and transmitted, the system automatically enters a dormant state, and automatically wakes up to repeat work when the next set shooting time comes;
and S5, finally, displaying data on the PC side or the mobile phone app.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116523833A (en) * | 2023-03-17 | 2023-08-01 | 苏交科集团股份有限公司 | Crack monitoring equipment based on image recognition technology and safety monitoring application platform comprising same |
| CN116856895A (en) * | 2023-07-06 | 2023-10-10 | 安徽井上天华科技有限公司 | Edge calculation data processing method based on high-frequency pressure crack monitoring |
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| CN116856895A (en) * | 2023-07-06 | 2023-10-10 | 安徽井上天华科技有限公司 | Edge calculation data processing method based on high-frequency pressure crack monitoring |
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