CN114152616A - Crack image recognition system and use method thereof - Google Patents
Crack image recognition system and use method thereof Download PDFInfo
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- CN114152616A CN114152616A CN202111198103.XA CN202111198103A CN114152616A CN 114152616 A CN114152616 A CN 114152616A CN 202111198103 A CN202111198103 A CN 202111198103A CN 114152616 A CN114152616 A CN 114152616A
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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Abstract
The invention discloses a crack image recognition system and a using method thereof, wherein the crack image recognition system comprises the following steps: the crack image recognition system is composed of hardware equipment, a crack detection device and a data processing device, wherein the hardware equipment comprises an ultrasonic generator, a computer, an ultrasonic output device, an infrared imager and a high-definition camera, and the ultrasonic generator, the ultrasonic output device, the infrared imager and the high-definition camera are all used as scanning devices of detected objects. The crack detection device comprises an LED light supplement source, a dot matrix laser and a control module, wherein a data processing device comprises a display module, a crack calculation module, a three-dimensional calculation module and an image processing module, the crack calculation module and the three-dimensional calculation module perform analysis and calculation through scanning data so as to obtain the size of a crack on the surface of an object, and meanwhile, a picture shot by a high-definition camera is analyzed and processed through the image processing module so as to identify the crack.
Description
Technical Field
The invention relates to the technical field of crack detection, in particular to a crack image recognition system and a use method thereof.
Background
With the continuous development of national infrastructure, reinforced cement structure buildings are spread all over the world, after the cement structure buildings are exposed to the sun and rain for a long time, damages such as stripping and cracks are easy to occur, and the crack detection of the cement structure buildings has important significance for ensuring the safety of the buildings, evaluating the service life of the buildings and the like.
The traditional crack detection device is fixed, needs a large amount of preparation work, has extremely low detection efficiency and low data processing automation degree, and needs to rely on manpower to judge the width and the length of a crack according to data such as photos and the like. Therefore, a new technical solution needs to be provided.
Disclosure of Invention
The invention aims to provide a crack image recognition system and a using method thereof, which solve the problems that the traditional crack detection device is fixed, needs a large amount of preparation work, has extremely low detection efficiency and low automation degree of data processing, and needs to depend on manpower to judge the width and the length of a crack according to data such as photos and the like.
In order to achieve the purpose, the invention provides the following technical scheme: a crack image recognition system and method of use thereof, comprising: the crack image recognition system is characterized by comprising hardware equipment, a crack detection device and a data processing device, wherein the hardware equipment comprises an ultrasonic generator, a computer, an ultrasonic output device, an infrared imager and a high-definition camera, the ultrasonic generator, the ultrasonic output device, the infrared imager and the high-definition camera are all used as scanning devices of detected objects, the crack detection device comprises an LED light supplementing source, a dot matrix laser and a control module, the data processing device comprises a display module, a crack calculation module, a three-dimensional calculation module and an image processing module, and the image processing module comprises a forming module and a processing module.
As a preferred embodiment of the present invention, the imaging module includes a lens, a linear array and a CCD imaging control circuit, and the lens, the linear array and the CCD imaging control circuit are connected to the high definition camera.
As a preferred embodiment of the invention, the processing module comprises an image acquisition module, a hardware circuit and a detected object data processing module.
As a preferred embodiment of the present invention, a light supplement module is connected to a lower portion of the CCD imaging control circuit, and the light supplement module is connected to a light supplement source of an LED.
As a preferred embodiment of the present invention, the method for using the crack image recognition system comprises the following steps:
step 1: scanning the detected object through an ultrasonic generator, an ultrasonic output device, a thermal infrared imager and a high-definition camera, and feeding back the scanning data to a computer;
step 2: analyzing and calculating through the scanning data by a crack calculating module and a three-dimensional calculating module so as to obtain the size of the crack on the surface of the object;
and step 3: meanwhile, the noise reduction and graying pretreatment are carried out on the picture shot by the high-definition camera to obtain a crack expansion gray image, and the crack expansion gray image is analyzed and processed by the image processing module to identify the crack;
and 4, step 4: carrying out threshold segmentation binarization processing on the preprocessed image to obtain a crack propagation binarization image;
and 5: carrying out continuous corrosion and opening operation processing on the obtained crack propagation binary image to obtain a crack skeleton image with crack width of one pixel point; obtaining the length of the crack framework according to a chain code method, and obtaining the actual crack length by carrying out scale conversion
Step 6: and (5) performing local fitting derivation by using an increasing polynomial method, determining the fatigue crack propagation rate and the fitting value of the crack length, processing the actual crack length and the related parameters obtained in the step (5), and calculating to obtain data of different stress intensity factor ranges delta K and corresponding crack propagation rates da/dN.
Compared with the prior art, the invention has the following beneficial effects:
the crack image recognition system comprises hardware equipment, a crack detection device and a data processing device, wherein the hardware equipment comprises an ultrasonic generator, a computer, an ultrasonic output device, an infrared thermal imager and a high-definition camera, the object to be detected is scanned by the ultrasonic generator, the ultrasonic output device, the infrared thermal imager and the high-definition camera, the scanning data is fed back to the computer, the crack detection device comprises an LED light supplementing source, a dot matrix laser and a control module, the data processing device comprises a display module, a crack calculation module, a three-dimensional calculation module and an image processing module, the crack calculation module and the three-dimensional calculation module perform analysis and calculation through the scanning data so as to obtain the size of the crack on the surface of the object, meanwhile, a picture shot by the high-definition camera is analyzed and processed by the image processing module so as to recognize the crack, and the detection efficiency is greatly improved, meanwhile, the automation degree of data processing is high, manual judgment is not needed, and the width and the length of the crack can be accurately detected.
Drawings
FIG. 1 is a schematic structural diagram of a crack image recognition system according to the present invention;
FIG. 2 is a diagram illustrating a hardware device according to the present invention;
FIG. 3 is a schematic structural diagram of a crack detection device according to the present invention;
FIG. 4 is a schematic diagram of a data processing apparatus according to the present invention;
FIG. 5 is a block diagram of an image processing module according to the present invention.
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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-5, the present invention provides a technical solution: a crack image recognition system and method of use thereof, comprising: the crack image recognition system comprises hardware equipment, a crack detection device and a data processing device, wherein the hardware equipment comprises an ultrasonic generator, a computer, an ultrasonic wave output device, an infrared imager and a high-definition camera, the ultrasonic generator, the ultrasonic wave output device, the infrared imager and the high-definition camera are all used as scanning devices of detected objects, the crack detection device comprises an LED light supplementing source, a dot matrix laser and a control module, the data processing device comprises a display module, a crack calculation module, a three-dimensional calculation module and an image processing module, the image processing module comprises a forming module and a processing module, the crack image recognition system comprises the hardware equipment, the crack detection device and the data processing device, the hardware equipment comprises the ultrasonic generator, the computer, the ultrasonic wave output device, the crack detection device and the data processing device, Thermal infrared imager and high definition camera, it is through supersonic generator, the ultrasonic output ware, thermal infrared imager and high definition camera scan the detection object, and with scan data feedback to computer, crack detection device includes LED light supplement source, dot matrix laser and control module, data processing apparatus includes display module, crack calculation module, three-dimensional calculation module and image processing module, crack calculation module and three-dimensional calculation module carry out the analysis and calculation through scan data, thereby reachs object surface crack size, the picture that high definition camera was shot simultaneously carries out analysis processes through image processing module, discern the crack, this setting has improved detection efficiency greatly, data processing degree of automation does simultaneously, need not to rely on the manual work to judge, the width and the length of detection crack that can be accurate.
Further improved, as shown in fig. 1: the imaging module comprises a lens, a linear array and a CCD imaging control circuit, the lens, the linear array and the CCD imaging control circuit are connected with the high-definition camera, the high-definition camera is better operated by the arrangement, and the accuracy is improved.
Further improved, as shown in fig. 1: the processing module comprises an image acquisition module, a hardware circuit and detection object data processing.
Further improved, as shown in fig. 1: the lower part of the CCD imaging control circuit is connected with a light supplementing module, and the light supplementing module is connected with an LED light supplementing source.
In a further improvement, the method for using the crack image identification system comprises the following steps:
step 1: scanning the detected object through an ultrasonic generator, an ultrasonic output device, a thermal infrared imager and a high-definition camera, and feeding back the scanning data to a computer;
step 2: analyzing and calculating through the scanning data by a crack calculating module and a three-dimensional calculating module so as to obtain the size of the crack on the surface of the object;
and step 3: meanwhile, the noise reduction and graying pretreatment are carried out on the picture shot by the high-definition camera to obtain a crack expansion gray image, and the crack expansion gray image is analyzed and processed by the image processing module to identify the crack;
and 4, step 4: carrying out threshold segmentation binarization processing on the preprocessed image to obtain a crack propagation binarization image;
and 5: carrying out continuous corrosion and opening operation processing on the obtained crack propagation binary image to obtain a crack skeleton image with crack width of one pixel point; obtaining the length of the crack framework according to a chain code method, and obtaining the actual crack length by carrying out scale conversion
Step 6: and (5) performing local fitting derivation by using an increasing polynomial method, determining the fatigue crack propagation rate and the fitting value of the crack length, processing the actual crack length and the related parameters obtained in the step (5), and calculating to obtain data of different stress intensity factor ranges delta K and corresponding crack propagation rates da/dN.
The crack image recognition system comprises hardware equipment, a crack detection device and a data processing device, wherein the hardware equipment comprises an ultrasonic generator, a computer, an ultrasonic output device, an infrared thermal imager and a high-definition camera, the object to be detected is scanned by the ultrasonic generator, the ultrasonic output device, the infrared thermal imager and the high-definition camera, the scanning data is fed back to the computer, the crack detection device comprises an LED light supplementing source, a dot matrix laser and a control module, the data processing device comprises a display module, a crack calculation module, a three-dimensional calculation module and an image processing module, the crack calculation module and the three-dimensional calculation module perform analysis and calculation through the scanning data so as to obtain the size of the crack on the surface of the object, meanwhile, a picture shot by the high-definition camera is analyzed and processed by the image processing module so as to recognize the crack, and the detection efficiency is greatly improved, meanwhile, the automation degree of data processing is high, manual judgment is not needed, and the width and the length of the crack can be accurately detected.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A crack image recognition system, characterized by: the method comprises the following steps: the crack image recognition system is characterized by comprising hardware equipment, a crack detection device and a data processing device, wherein the hardware equipment comprises an ultrasonic generator, a computer, an ultrasonic output device, an infrared imager and a high-definition camera, the ultrasonic generator, the ultrasonic output device, the infrared imager and the high-definition camera are all used as scanning devices of detected objects, the crack detection device comprises an LED light supplementing source, a dot matrix laser and a control module, the data processing device comprises a display module, a crack calculation module, a three-dimensional calculation module and an image processing module, and the image processing module comprises a forming module and a processing module.
2. A crack image recognition system as claimed in claim 1, wherein: the imaging module comprises a lens, a linear array and a CCD imaging control circuit, and the lens, the linear array and the CCD imaging control circuit are connected with the high-definition camera.
3. A crack image recognition system as claimed in claim 1, wherein: the processing module comprises an image acquisition module, a hardware circuit and detection object data processing.
4. A crack image recognition system as claimed in claim 1, wherein: the lower part of the CCD imaging control circuit is connected with a light supplementing module, and the light supplementing module is connected with an LED light supplementing source.
5. The method of using a crack image recognition system as claimed in claim 1, wherein: the use method of the crack image identification system comprises the following steps:
step 1: scanning the detected object through an ultrasonic generator, an ultrasonic output device, a thermal infrared imager and a high-definition camera, and feeding back the scanning data to a computer;
step 2: analyzing and calculating through the scanning data by a crack calculating module and a three-dimensional calculating module so as to obtain the size of the crack on the surface of the object;
and step 3: meanwhile, the noise reduction and graying pretreatment are carried out on the picture shot by the high-definition camera to obtain a crack propagation gray image, and the crack propagation gray image is analyzed and processed by the image processing module to identify the crack;
and 4, step 4: carrying out threshold segmentation binarization processing on the preprocessed image to obtain a crack propagation binarization image;
and 5: carrying out continuous corrosion and opening operation processing on the obtained crack propagation binary image to obtain a crack skeleton image with the crack width being one pixel point; obtaining the length of the crack framework according to a chain code method, and carrying out scale conversion to obtain the actual crack length
Step 6: and (5) performing local fitting derivation by using an increasing polynomial method, determining the fatigue crack propagation rate and the fitting value of the crack length, processing the actual crack length and the related parameters obtained in the step (5), and calculating to obtain data of different stress intensity factor ranges delta K and corresponding crack propagation rates da/dN.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118083510A (en) * | 2024-04-23 | 2024-05-28 | 四川省丹丹郫县豆瓣集团股份有限公司 | Bottled thick broad-bean sauce goes out product spot-checking device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102692188A (en) * | 2012-05-08 | 2012-09-26 | 浙江工业大学 | Dynamic crack length measurement method for machine vision fatigue crack propagation test |
| CN108287164A (en) * | 2017-12-23 | 2018-07-17 | 深圳天眼激光科技有限公司 | A kind of flaw detection system |
| CN108956668A (en) * | 2018-07-23 | 2018-12-07 | 湖南大学 | A kind of crack tip ope ning angle degree measurement method based on SEM in situ |
| CN109459492A (en) * | 2018-10-17 | 2019-03-12 | 山东省科学院海洋仪器仪表研究所 | The optoacoustic photo-thermal complex detection system and method for invar steel sheet weld crack defect |
| CN110009606A (en) * | 2019-03-22 | 2019-07-12 | 北京航空航天大学 | A kind of crack propagation dynamic monitoring method and device based on image recognition |
-
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- 2021-10-14 CN CN202111198103.XA patent/CN114152616A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102692188A (en) * | 2012-05-08 | 2012-09-26 | 浙江工业大学 | Dynamic crack length measurement method for machine vision fatigue crack propagation test |
| CN108287164A (en) * | 2017-12-23 | 2018-07-17 | 深圳天眼激光科技有限公司 | A kind of flaw detection system |
| CN108956668A (en) * | 2018-07-23 | 2018-12-07 | 湖南大学 | A kind of crack tip ope ning angle degree measurement method based on SEM in situ |
| CN109459492A (en) * | 2018-10-17 | 2019-03-12 | 山东省科学院海洋仪器仪表研究所 | The optoacoustic photo-thermal complex detection system and method for invar steel sheet weld crack defect |
| CN110009606A (en) * | 2019-03-22 | 2019-07-12 | 北京航空航天大学 | A kind of crack propagation dynamic monitoring method and device based on image recognition |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118083510A (en) * | 2024-04-23 | 2024-05-28 | 四川省丹丹郫县豆瓣集团股份有限公司 | Bottled thick broad-bean sauce goes out product spot-checking device |
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