CN112834457A - Metal microcrack three-dimensional characterization system and method based on reflective laser thermal imaging - Google Patents
Metal microcrack three-dimensional characterization system and method based on reflective laser thermal imaging Download PDFInfo
- Publication number
- CN112834457A CN112834457A CN202110092250.2A CN202110092250A CN112834457A CN 112834457 A CN112834457 A CN 112834457A CN 202110092250 A CN202110092250 A CN 202110092250A CN 112834457 A CN112834457 A CN 112834457A
- Authority
- CN
- China
- Prior art keywords
- thermal infrared
- image
- infrared image
- laser
- metal
- 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.)
- Granted
Links
- 239000002184 metal Substances 0.000 title claims abstract description 64
- 229910052751 metal Inorganic materials 0.000 title claims abstract description 64
- 238000012512 characterization method Methods 0.000 title claims abstract description 40
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000001931 thermography Methods 0.000 title claims abstract description 25
- 239000004065 semiconductor Substances 0.000 claims abstract description 34
- 238000001514 detection method Methods 0.000 claims abstract description 7
- 238000004093 laser heating Methods 0.000 claims description 25
- 238000001914 filtration Methods 0.000 claims description 12
- 238000010438 heat treatment Methods 0.000 claims description 12
- 238000000354 decomposition reaction Methods 0.000 claims description 9
- 230000017525 heat dissipation Effects 0.000 claims description 9
- 238000000605 extraction Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 10
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 238000009529 body temperature measurement Methods 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 150000002739 metals Chemical class 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/39—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using tunable lasers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/48—Thermography; Techniques using wholly visual means
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/171—Systems in which incident light is modified in accordance with the properties of the material investigated with calorimetric detection, e.g. with thermal lens detection
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/55—Specular reflectivity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J2005/0077—Imaging
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1765—Method using an image detector and processing of image signal
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10048—Infrared image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30136—Metal
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Optics & Photonics (AREA)
- Quality & Reliability (AREA)
- Geometry (AREA)
- Investigating Or Analyzing Materials Using Thermal Means (AREA)
Abstract
The invention relates to a metal surface microcrack characterization technology, in particular to a reflective laser thermal imaging-based metal microcrack three-dimensional characterization system and method. The method solves the problem that the traditional metal surface microcrack characterization technology has incomplete characterization results. The metal microcrack three-dimensional characterization system based on the reflective laser thermal imaging comprises a metal workpiece to be detected, a Fourier lens, a semiconductor laser, a computer, a signal generator and a thermal infrared imager; the exit end of the semiconductor laser is over against the incident end of the Fourier lens; the exit end of the Fourier lens is over against the front surface of the metal workpiece to be detected; the signal output end of the computer and the signal output end of the signal generator are connected with the signal input end of the semiconductor laser; and the detection end of the thermal infrared imager is obliquely opposite to the front surface of the metal workpiece to be detected. The method is suitable for characterization of the microcracks on the metal surface.
Description
Technical Field
The invention relates to a metal surface microcrack characterization technology, in particular to a reflective laser thermal imaging-based metal microcrack three-dimensional characterization system and method.
Background
Metals are widely used in the manufacture of aircraft engines, automobiles, ships and the like because of their advantages of high strength, good heat resistance, strong corrosion resistance and the like. In the use process of the metal workpiece, surface microcracks are easy to appear due to fatigue aging or severe environment, so that the mechanical property, vibration noise and service life of the metal workpiece are influenced. Therefore, in order to ensure the mechanical property, vibration noise and service life of the metal workpiece, the metal workpiece needs to be subjected to surface microcrack characterization. However, the conventional metal surface microcrack characterization technology is limited by its principle, and can only perform two-dimensional characterization of the surface microcrack, but cannot perform three-dimensional characterization of the surface microcrack, thereby resulting in incomplete characterization results. Therefore, a metal microcrack three-dimensional characterization system and a metal microcrack three-dimensional characterization method based on reflective laser thermal imaging are needed to be invented to solve the problem that the characterization result of the traditional metal surface microcrack characterization technology is incomplete.
Disclosure of Invention
The invention provides a reflective laser thermal imaging-based metal microcrack three-dimensional characterization system and method, aiming at solving the problem that the characterization result of the traditional metal surface microcrack characterization technology is incomplete.
The invention is realized by adopting the following technical scheme:
the metal microcrack three-dimensional characterization system based on the reflective laser thermal imaging comprises a metal workpiece to be detected, a Fourier lens, a semiconductor laser, a computer, a signal generator and a thermal infrared imager; the exit end of the semiconductor laser is over against the incident end of the Fourier lens; the exit end of the Fourier lens is over against the front surface of the metal workpiece to be detected; the signal output end of the computer and the signal output end of the signal generator are connected with the signal input end of the semiconductor laser; and the detection end of the thermal infrared imager is obliquely opposite to the front surface of the metal workpiece to be detected.
The invention discloses a metal microcrack three-dimensional characterization method based on reflective laser thermal imaging (the method is realized based on a metal microcrack three-dimensional characterization system based on reflective laser thermal imaging), which is realized by adopting the following steps:
the method comprises the following steps: aiming at a certain micro-crack appearing on the front surface of the detected metal workpiece, selecting a laser heating point on the front surface of the detected metal workpiece, wherein the laser heating point is positioned 2mm under the micro-crack;
step two: setting the output power of the semiconductor laser through a computer; setting the output pulse width of the semiconductor laser through a signal generator; then, starting a semiconductor laser and a thermal infrared imager, wherein the semiconductor laser emits a pulse laser beam, and the pulse laser beam is focused by a Fourier lens and then vertically irradiates a laser heating point, so that the laser heating point is heated; when the heating time reaches 1s, the semiconductor laser is closed, so that the laser heating point radiates; when the heat dissipation time reaches 1s, closing the thermal infrared imager; in the heating and radiating processes, the thermal infrared imager detects the temperature field change of the front surface of the detected metal workpiece in real time and generates a thermal infrared image sequence in real time according to the detection result, wherein the thermal infrared image sequence comprises a plurality of frames of thermal infrared images;
step three: homomorphic filtering is carried out on each frame of thermal infrared image, so that multiplicative noise of each frame of thermal infrared image is filtered;
step four: performing two-layer wavelet decomposition on each frame of thermal infrared image, thereby calculating the noise variance of each frame of thermal infrared image;
step five: according to the noise variance of each frame of thermal infrared image, performing similar block matching on each frame of thermal infrared image by using a three-dimensional block matching algorithm to obtain a similar block set of each frame of thermal infrared image;
step six: accumulating the similar blocks of each frame of thermal infrared image into a similar block matrix, and sequentially carrying out two-dimensional discrete cosine transform, one-dimensional Haar wavelet transform, threshold denoising and exponential transform on the similar block matrix of each frame of thermal infrared image, thereby completing the processing of each frame of thermal infrared image;
step seven: selecting one frame of thermal infrared image in the heat dissipation process from the processed frames of thermal infrared images as a first detected image, converting the first detected image into a gray image, and then segmenting the gray image into a binary image by using a maximum inter-class variance method;
step eight: calculating the number of pixel points of each closed region in the binary image by using an eight-connectivity method, and then filtering out the closed regions with the number of the pixel points less than 4;
step nine: performing edge extraction on the binary image to obtain a contour of the microcrack;
step ten: calculating the contour area of the microcracks;
step eleven: selecting one frame of thermal infrared image in the heating process from the processed frames of thermal infrared images as a second detected image, performing fast Fourier transform on 8 line segments at 24 pixel points away from the laser heating point in the second detected image, and extracting Fourier value change curves of the 8 line segments;
step twelve: and respectively obtaining Fourier values at 15 pixel points above the laser heating point in the second detected image and Fourier values at 15 pixel points below the laser heating point in the second detected image according to the Fourier change curves of the 8 line segments, calculating the difference value of the two Fourier values, and then quantifying the depth of the microcrack according to the difference value of the two Fourier values.
In the second step, the output power of the semiconductor laser is set to 50W, and the output pulse width of the semiconductor laser is set to 1 s.
In the third step, the homomorphic filtering formula is as follows:
in the formula:
representing the thermal infrared image before homomorphic filtering;
representing a homomorphically filtered thermal infrared image; n is1Representing multiplicative noise of the thermal infrared image.
In the fourth step, a noise variance calculation formula of the thermal infrared image is as follows:
in the formula: e represents the noise variance of the thermal infrared image; m1Representing the median of the diagonal components of the first layer in a two-layer wavelet decomposition; m2Representing the median of the second-level diagonal components in a two-level wavelet decomposition.
In the fifth step, the set of similar blocks of the thermal infrared image is represented as follows:
in the formula:
a set of similar blocks representing a thermal infrared image;
representing a target block; zxRepresents a search block;e represents the noise variance of the thermal infrared image.
And in the seventh step, selecting a frame of thermal infrared image with the heat dissipation time reaching 0.75s as a first detected image, and converting the first detected image into a gray image by utilizing an im2bw function.
In the ninth step, the edge extraction process is as follows: firstly, the contours of the remaining closed regions in the binary image are marked by utilizing a bwbounderies function, then the boundaries of the contours are connected by utilizing a line function, and then the minimum circumscribed rectangle of the boundaries is divided by utilizing a minboundry function, so that the contours of the microcracks are obtained.
In the step ten, the area of the contour of the microcrack is calculated by using the area function.
And in the eleventh step, selecting a frame of thermal infrared image with the heating time reaching 1s as a second detected image.
Compared with the traditional metal surface microcrack characterization technology, the reflective laser thermal imaging-based metal microcrack three-dimensional characterization system and method disclosed by the invention are based on the laser heating technology and the infrared temperature measurement technology, and are combined with a brand-new image processing flow, so that the outline area characterization of the microcracks is realized on one hand, and the depth characterization of the microcracks is realized on the other hand, and therefore, the three-dimensional characterization of the surface microcracks of the metal workpiece is realized, and the characterization result is more comprehensive.
The method effectively solves the problem that the traditional metal surface microcrack characterization technology has incomplete characterization results, and is suitable for metal surface microcrack characterization.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
In the figure: 1-a metal workpiece to be detected, 2-a Fourier lens, 3-a semiconductor laser, 4-a computer, 5-a signal generator and 6-a thermal infrared imager.
Detailed Description
The metal microcrack three-dimensional characterization system based on the reflective laser thermal imaging comprises a metal workpiece to be detected 1, a Fourier lens 2, a semiconductor laser 3, a computer 4, a signal generator 5 and a thermal infrared imager 6; wherein, the exit end of the semiconductor laser 3 is over against the incident end of the Fourier lens 2; the exit end of the Fourier lens 2 is over against the front surface of the metal workpiece 1 to be detected; the signal output end of the computer 4 and the signal output end of the signal generator 5 are both connected with the signal input end of the semiconductor laser 3; the detection end of the thermal infrared imager 6 is obliquely opposite to the front surface of the metal workpiece 1 to be detected.
The invention discloses a metal microcrack three-dimensional characterization method based on reflective laser thermal imaging (the method is realized based on a metal microcrack three-dimensional characterization system based on reflective laser thermal imaging), which is realized by adopting the following steps:
the method comprises the following steps: aiming at a certain micro-crack appearing on the front surface of the detected metal workpiece 1, selecting a laser heating point on the front surface of the detected metal workpiece 1, wherein the laser heating point is positioned 2mm under the micro-crack;
step two: setting the output power of the semiconductor laser 3 by the computer 4; the output pulse width of the semiconductor laser 3 is set through the signal generator 5; then, starting the semiconductor laser 3 and the thermal infrared imager 6, wherein the semiconductor laser 3 emits a pulse laser beam, and the pulse laser beam is focused by the Fourier lens 2 and then vertically irradiates a laser heating point, so that the laser heating point is heated; when the heating time reaches 1s, the semiconductor laser 3 is closed, so that the laser heating point radiates; when the heat dissipation time reaches 1s, the thermal infrared imager 6 is closed; in the heating and radiating processes, the thermal infrared imager 6 detects the temperature field change of the front surface of the detected metal workpiece 1 in real time and generates a thermal infrared image sequence in real time according to the detection result, wherein the thermal infrared image sequence comprises a plurality of frames of thermal infrared images;
step three: homomorphic filtering is carried out on each frame of thermal infrared image, so that multiplicative noise of each frame of thermal infrared image is filtered;
step four: performing two-layer wavelet decomposition on each frame of thermal infrared image, thereby calculating the noise variance of each frame of thermal infrared image;
step five: according to the noise variance of each frame of thermal infrared image, performing similar block matching on each frame of thermal infrared image by using a three-dimensional block matching algorithm to obtain a similar block set of each frame of thermal infrared image;
step six: accumulating the similar blocks of each frame of thermal infrared image into a similar block matrix, and sequentially carrying out two-dimensional discrete cosine transform, one-dimensional Haar wavelet transform, threshold denoising and exponential transform on the similar block matrix of each frame of thermal infrared image, thereby completing the processing of each frame of thermal infrared image;
step seven: selecting one frame of thermal infrared image in the heat dissipation process from the processed frames of thermal infrared images as a first detected image, converting the first detected image into a gray image, and then segmenting the gray image into a binary image by using a maximum inter-class variance method;
step eight: calculating the number of pixel points of each closed region in the binary image by using an eight-connectivity method, and then filtering out the closed regions with the number of the pixel points less than 4;
step nine: performing edge extraction on the binary image to obtain a contour of the microcrack;
step ten: calculating the contour area of the microcracks;
step eleven: selecting one frame of thermal infrared image in the heating process from the processed frames of thermal infrared images as a second detected image, performing fast Fourier transform on 8 line segments at 24 pixel points away from the laser heating point in the second detected image, and extracting Fourier value change curves of the 8 line segments;
step twelve: and respectively obtaining Fourier values at 15 pixel points above the laser heating point in the second detected image and Fourier values at 15 pixel points below the laser heating point in the second detected image according to the Fourier change curves of the 8 line segments, calculating the difference value of the two Fourier values, and then quantifying the depth of the microcrack according to the difference value of the two Fourier values.
In the second step, the output power of the semiconductor laser 3 is set to 50W, and the output pulse width of the semiconductor laser 3 is set to 1 s.
In the third step, the homomorphic filtering formula is as follows:
in the formula:
representing the thermal infrared image before homomorphic filtering;
representing a homomorphically filtered thermal infrared image; n is1Representing multiplicative noise of the thermal infrared image.
In the fourth step, a noise variance calculation formula of the thermal infrared image is as follows:
in the formula: e represents the noise variance of the thermal infrared image; m1Representing the median of the diagonal components of the first layer in a two-layer wavelet decomposition; m2Representing the median of the second-level diagonal components in a two-level wavelet decomposition.
In the fifth step, the set of similar blocks of the thermal infrared image is represented as follows:
in the formula:
a set of similar blocks representing a thermal infrared image;
representing a target block; zxRepresents a search block; e represents the noise variance of the thermal infrared image.
And in the seventh step, selecting a frame of thermal infrared image with the heat dissipation time reaching 0.75s as a first detected image, and converting the first detected image into a gray image by utilizing an im2bw function.
In the ninth step, the edge extraction process is as follows: firstly, the contours of the remaining closed regions in the binary image are marked by utilizing a bwbounderies function, then the boundaries of the contours are connected by utilizing a line function, and then the minimum circumscribed rectangle of the boundaries is divided by utilizing a minboundry function, so that the contours of the microcracks are obtained.
In the step ten, the area of the contour of the microcrack is calculated by using the area function.
And in the eleventh step, selecting a frame of thermal infrared image with the heating time reaching 1s as a second detected image.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (10)
1. A metal microcrack three-dimensional characterization system based on reflective laser thermal imaging is characterized in that: the device comprises a metal workpiece to be detected (1), a Fourier lens (2), a semiconductor laser (3), a computer (4), a signal generator (5) and a thermal infrared imager (6); wherein the emergent end of the semiconductor laser (3) is over against the incident end of the Fourier lens (2); the exit end of the Fourier lens (2) is just opposite to the front surface of the metal workpiece (1) to be detected; the signal output end of the computer (4) and the signal output end of the signal generator (5) are connected with the signal input end of the semiconductor laser (3); the detection end of the thermal infrared imager (6) is obliquely opposite to the front surface of the metal workpiece (1) to be detected.
2. A metal microcrack three-dimensional characterization method based on reflective laser thermal imaging, which is realized based on the metal microcrack three-dimensional characterization system based on reflective laser thermal imaging of claim 1, and is characterized in that: the method is realized by adopting the following steps:
the method comprises the following steps: aiming at a certain micro-crack appearing on the front surface of the detected metal workpiece (1), selecting a laser heating point on the front surface of the detected metal workpiece (1), wherein the laser heating point is positioned 2mm under the micro-crack;
step two: setting the output power of the semiconductor laser (3) through a computer (4); setting the output pulse width of the semiconductor laser (3) through a signal generator (5); then, starting the semiconductor laser (3) and the thermal infrared imager (6), wherein the semiconductor laser (3) emits a pulse laser beam, and the pulse laser beam is focused by the Fourier lens (2) and then vertically irradiates a laser heating point, so that the laser heating point is heated; when the heating time reaches 1s, the semiconductor laser (3) is closed, so that the laser heating point radiates; when the heat dissipation time reaches 1s, the thermal infrared imager (6) is closed; in the heating and radiating processes, the thermal infrared imager (6) detects the temperature field change of the front surface of the detected metal workpiece (1) in real time and generates a thermal infrared image sequence in real time according to the detection result, wherein the thermal infrared image sequence comprises a plurality of frames of thermal infrared images;
step three: homomorphic filtering is carried out on each frame of thermal infrared image, so that multiplicative noise of each frame of thermal infrared image is filtered;
step four: performing two-layer wavelet decomposition on each frame of thermal infrared image, thereby calculating the noise variance of each frame of thermal infrared image;
step five: according to the noise variance of each frame of thermal infrared image, performing similar block matching on each frame of thermal infrared image by using a three-dimensional block matching algorithm to obtain a similar block set of each frame of thermal infrared image;
step six: accumulating the similar blocks of each frame of thermal infrared image into a similar block matrix, and sequentially carrying out two-dimensional discrete cosine transform, one-dimensional Haar wavelet transform, threshold denoising and exponential transform on the similar block matrix of each frame of thermal infrared image, thereby completing the processing of each frame of thermal infrared image;
step seven: selecting one frame of thermal infrared image in the heat dissipation process from the processed frames of thermal infrared images as a first detected image, converting the first detected image into a gray image, and then segmenting the gray image into a binary image by using a maximum inter-class variance method;
step eight: calculating the number of pixel points of each closed region in the binary image by using an eight-connectivity method, and then filtering out the closed regions with the number of the pixel points less than 4;
step nine: performing edge extraction on the binary image to obtain a contour of the microcrack;
step ten: calculating the contour area of the microcracks;
step eleven: selecting one frame of thermal infrared image in the heating process from the processed frames of thermal infrared images as a second detected image, performing fast Fourier transform on 8 line segments at 24 pixel points away from the laser heating point in the second detected image, and extracting Fourier value change curves of the 8 line segments;
step twelve: and respectively obtaining Fourier values at 15 pixel points above the laser heating point in the second detected image and Fourier values at 15 pixel points below the laser heating point in the second detected image according to the Fourier change curves of the 8 line segments, calculating the difference value of the two Fourier values, and then quantifying the depth of the microcrack according to the difference value of the two Fourier values.
3. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 2, wherein: in the second step, the output power of the semiconductor laser (3) is set to 50W, and the output pulse width of the semiconductor laser (3) is set to 1 s.
4. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 2, wherein: in the third step, the homomorphic filtering formula is as follows:
in the formula:
representing the thermal infrared image before homomorphic filtering;
representing a homomorphically filtered thermal infrared image; n is1Representing multiplicative noise of the thermal infrared image.
5. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 2, wherein: in the fourth step, a noise variance calculation formula of the thermal infrared image is as follows:
in the formula: e represents the noise variance of the thermal infrared image; m1Representing the median of the diagonal components of the first layer in a two-layer wavelet decomposition; m2Representing the median of the second-level diagonal components in a two-level wavelet decomposition.
6. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 2, wherein: in the fifth step, the set of similar blocks of the thermal infrared image is represented as follows:
in the formula:
a set of similar blocks representing a thermal infrared image;
representing a target block; zxRepresents a search block; e represents the noise variance of the thermal infrared image.
7. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 2, wherein: and in the seventh step, selecting a frame of thermal infrared image with the heat dissipation time reaching 0.75s as a first detected image, and converting the first detected image into a gray image by utilizing an im2bw function.
8. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 2, wherein: in the ninth step, the edge extraction process is as follows: firstly, the contours of the remaining closed regions in the binary image are marked by utilizing a bwbounderies function, then the boundaries of the contours are connected by utilizing a line function, and then the minimum circumscribed rectangle of the boundaries is divided by utilizing a minboundry function, so that the contours of the microcracks are obtained.
9. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 2, wherein: in the step ten, the area of the contour of the microcrack is calculated by using the area function.
10. The method for three-dimensionally characterizing metal microcracks based on reflective laser thermal imaging according to claim 2, wherein: and in the eleventh step, selecting a frame of thermal infrared image with the heating time reaching 1s as a second detected image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110092250.2A CN112834457B (en) | 2021-01-23 | 2021-01-23 | Metal microcrack three-dimensional characterization system and method based on reflective laser thermal imaging |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110092250.2A CN112834457B (en) | 2021-01-23 | 2021-01-23 | Metal microcrack three-dimensional characterization system and method based on reflective laser thermal imaging |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112834457A true CN112834457A (en) | 2021-05-25 |
| CN112834457B CN112834457B (en) | 2022-06-03 |
Family
ID=75931241
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110092250.2A Active CN112834457B (en) | 2021-01-23 | 2021-01-23 | Metal microcrack three-dimensional characterization system and method based on reflective laser thermal imaging |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112834457B (en) |
Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030116725A1 (en) * | 2001-12-21 | 2003-06-26 | Kimberly-Clark Worldwide, Inc. | Web detection with gradient-indexed optics |
| KR20050054647A (en) * | 2003-12-05 | 2005-06-10 | 삼성전기주식회사 | Visual inspection appratus for screening crack on chip component edge |
| US20060232583A1 (en) * | 2000-03-28 | 2006-10-19 | Michael Petrov | System and method of three-dimensional image capture and modeling |
| CN103674453A (en) * | 2013-12-26 | 2014-03-26 | 南京诺威尔光电系统有限公司 | Thermal wave imaging gas leakage detecting device and method |
| WO2014066226A1 (en) * | 2012-10-22 | 2014-05-01 | Qiagen Gaithersburg, Inc. | Automated pelletized sample vision inspection apparatus |
| CN104240252A (en) * | 2014-09-22 | 2014-12-24 | 电子科技大学 | Detecting Algorithm for cracks of surface of high-temperature billet of machine vision bar |
| CN104777049A (en) * | 2015-03-27 | 2015-07-15 | 中国人民解放军装甲兵工程学院 | Crankshaft remanufacturing life assessment system and method thereof |
| US20170023505A1 (en) * | 2015-07-22 | 2017-01-26 | Leonardo S.P.A. | Method and system of thermographic non-destructive inspection for detecting and measuring volumetric defects in composite material structures |
| CN107610104A (en) * | 2017-08-31 | 2018-01-19 | 南通兴华达高实业有限公司 | Crack detecting method at a kind of elevator compensation chain R based on machine vision |
| CN108986081A (en) * | 2018-06-28 | 2018-12-11 | 湖南红太阳新能源科技有限公司 | Photovoltaic component glass crack detecting method, device, equipment and storage medium |
| CN109254012A (en) * | 2018-10-09 | 2019-01-22 | 中北大学 | A kind of cracks of metal surface detection device and method based on semiconductor laser |
| CN109716108A (en) * | 2016-12-30 | 2019-05-03 | 同济大学 | A kind of Asphalt Pavement Damage detection system based on binocular image analysis |
| US20190178808A1 (en) * | 2017-12-08 | 2019-06-13 | Infineon Technologies Ag | Inspection Method for Semiconductor Substrates Using Slope Data and Inspection Apparatus |
| CN109886921A (en) * | 2019-01-16 | 2019-06-14 | 新而锐电子科技(上海)有限公司 | Crack size measure, device and electronic equipment based on digital picture |
| CN110223296A (en) * | 2019-07-08 | 2019-09-10 | 山东建筑大学 | A kind of screw-thread steel detection method of surface flaw and system based on machine vision |
| CN110246118A (en) * | 2019-05-07 | 2019-09-17 | 中国人民解放军陆军工程大学 | A kind of depth of defect detection method |
| CN110400311A (en) * | 2019-08-01 | 2019-11-01 | 中北大学 | High-temperature alloy surface defect characteristic extracting method based on pulse laser thermal imaging |
| CN110942457A (en) * | 2019-11-30 | 2020-03-31 | 天津大学 | Solar cell panel defect detection method based on digital image processing technology |
| CN111008956A (en) * | 2019-11-13 | 2020-04-14 | 武汉工程大学 | Beam bottom crack detection method, system, device and medium based on image processing |
| CN111189543A (en) * | 2020-01-15 | 2020-05-22 | 大连理工大学 | On-line calibration method for emissivity of thermal infrared imager in additive manufacturing |
| CN111537564A (en) * | 2020-06-16 | 2020-08-14 | 中北大学 | Metal microcrack depth detection system and method based on transmission type laser thermal imaging |
| CN112243518A (en) * | 2019-08-29 | 2021-01-19 | 深圳市大疆创新科技有限公司 | Method and device for acquiring depth map and computer storage medium |
-
2021
- 2021-01-23 CN CN202110092250.2A patent/CN112834457B/en active Active
Patent Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060232583A1 (en) * | 2000-03-28 | 2006-10-19 | Michael Petrov | System and method of three-dimensional image capture and modeling |
| US20030116725A1 (en) * | 2001-12-21 | 2003-06-26 | Kimberly-Clark Worldwide, Inc. | Web detection with gradient-indexed optics |
| KR20050054647A (en) * | 2003-12-05 | 2005-06-10 | 삼성전기주식회사 | Visual inspection appratus for screening crack on chip component edge |
| WO2014066226A1 (en) * | 2012-10-22 | 2014-05-01 | Qiagen Gaithersburg, Inc. | Automated pelletized sample vision inspection apparatus |
| CN103674453A (en) * | 2013-12-26 | 2014-03-26 | 南京诺威尔光电系统有限公司 | Thermal wave imaging gas leakage detecting device and method |
| CN104240252A (en) * | 2014-09-22 | 2014-12-24 | 电子科技大学 | Detecting Algorithm for cracks of surface of high-temperature billet of machine vision bar |
| CN104777049A (en) * | 2015-03-27 | 2015-07-15 | 中国人民解放军装甲兵工程学院 | Crankshaft remanufacturing life assessment system and method thereof |
| US20170023505A1 (en) * | 2015-07-22 | 2017-01-26 | Leonardo S.P.A. | Method and system of thermographic non-destructive inspection for detecting and measuring volumetric defects in composite material structures |
| CN109716108A (en) * | 2016-12-30 | 2019-05-03 | 同济大学 | A kind of Asphalt Pavement Damage detection system based on binocular image analysis |
| CN107610104A (en) * | 2017-08-31 | 2018-01-19 | 南通兴华达高实业有限公司 | Crack detecting method at a kind of elevator compensation chain R based on machine vision |
| US20190178808A1 (en) * | 2017-12-08 | 2019-06-13 | Infineon Technologies Ag | Inspection Method for Semiconductor Substrates Using Slope Data and Inspection Apparatus |
| CN108986081A (en) * | 2018-06-28 | 2018-12-11 | 湖南红太阳新能源科技有限公司 | Photovoltaic component glass crack detecting method, device, equipment and storage medium |
| CN109254012A (en) * | 2018-10-09 | 2019-01-22 | 中北大学 | A kind of cracks of metal surface detection device and method based on semiconductor laser |
| CN109886921A (en) * | 2019-01-16 | 2019-06-14 | 新而锐电子科技(上海)有限公司 | Crack size measure, device and electronic equipment based on digital picture |
| CN110246118A (en) * | 2019-05-07 | 2019-09-17 | 中国人民解放军陆军工程大学 | A kind of depth of defect detection method |
| CN110223296A (en) * | 2019-07-08 | 2019-09-10 | 山东建筑大学 | A kind of screw-thread steel detection method of surface flaw and system based on machine vision |
| CN110400311A (en) * | 2019-08-01 | 2019-11-01 | 中北大学 | High-temperature alloy surface defect characteristic extracting method based on pulse laser thermal imaging |
| CN112243518A (en) * | 2019-08-29 | 2021-01-19 | 深圳市大疆创新科技有限公司 | Method and device for acquiring depth map and computer storage medium |
| CN111008956A (en) * | 2019-11-13 | 2020-04-14 | 武汉工程大学 | Beam bottom crack detection method, system, device and medium based on image processing |
| CN110942457A (en) * | 2019-11-30 | 2020-03-31 | 天津大学 | Solar cell panel defect detection method based on digital image processing technology |
| CN111189543A (en) * | 2020-01-15 | 2020-05-22 | 大连理工大学 | On-line calibration method for emissivity of thermal infrared imager in additive manufacturing |
| CN111537564A (en) * | 2020-06-16 | 2020-08-14 | 中北大学 | Metal microcrack depth detection system and method based on transmission type laser thermal imaging |
Non-Patent Citations (6)
| Title |
|---|
| TSE, PW (TSE, PETER W.) 等: "Sub-surface defects detection of by using active thermography and advanced image edge detection", 《12TH INTERNATIONAL CONFERENCE ON DAMAGE ASSESSMENT OF STRUCTURES》 * |
| TSE, PW (TSE, PETER W.) 等: "Sub-surface defects detection of by using active thermography and advanced image edge detection", 《12TH INTERNATIONAL CONFERENCE ON DAMAGE ASSESSMENT OF STRUCTURES》, vol. 842, 19 November 2017 (2017-11-19), pages 842 * |
| 刘佳琪 等: "基于激光热成像的金属表面缺陷深度检测", 《激光与光电子学进展》 * |
| 刘佳琪 等: "基于激光热成像的金属表面缺陷深度检测", 《激光与光电子学进展》, vol. 58, no. 4, 3 September 2020 (2020-09-03), pages 307 - 314 * |
| 朱华: "钢坯表面裂纹的图像识别算法研究", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
| 朱华: "钢坯表面裂纹的图像识别算法研究", 《中国优秀硕士学位论文全文数据库信息科技辑》, no. 4, 15 April 2014 (2014-04-15), pages 138 - 951 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112834457B (en) | 2022-06-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Cao et al. | Segmentation and enhancement of latent fingerprints: A coarse to fine ridgestructure dictionary | |
| CN114972326A (en) | Defective product identification method for heat-shrinkable tube expanding process | |
| CN109509163B (en) | FGF-based multi-focus image fusion method and system | |
| Lestriandoko et al. | Circle detection based on hough transform and Mexican Hat filter | |
| CN104091145A (en) | Human palm vein feature image acquisition method | |
| Liu et al. | Iterating tensor voting: A perceptual grouping approach for crack detection on EL images | |
| CN113516619A (en) | Product surface flaw identification method based on image processing technology | |
| Ashoori-Lalimi et al. | An efficient method for vehicle license plate detection in complex scenes | |
| CN116363122A (en) | Steel weld crack detection method and system based on artificial intelligence | |
| CN116051992A (en) | Crack identification method | |
| CN112834457B (en) | Metal microcrack three-dimensional characterization system and method based on reflective laser thermal imaging | |
| Wu et al. | Research on crack detection algorithm of asphalt pavement | |
| Ahmed et al. | Recent approaches on no-reference image quality assessment for contrast distortion images with multiscale geometric analysis transforms: A survey | |
| Wu et al. | Research on morphological wavelet operator for crack detection of asphalt pavement | |
| CN112435186B (en) | Fingerprint image enhancement method based on double-rule matrix direction field | |
| Yang et al. | Optimum color and contrast enhancement for online ferrography image restoration | |
| Yamazaki et al. | Defect detection for forged metal parts by image processing | |
| Zeng et al. | A comparison study of CNN denoisers on PRNU extraction | |
| Biswas et al. | A new approach of edge detection via polynomial evaluation based gabor filter | |
| Biswas | Impulse noise suppression in color images using median filter and deep learning | |
| Huang et al. | Classifier fusion-based vehicle license plate detection algorithm | |
| CN110674681A (en) | Identity verification method and device based on attention mechanism | |
| Lou et al. | Image enhancement of palm veins based on adaptive fusion and Gabor filter | |
| CN118013204B (en) | Method and device for denoising radar modulation signal time-frequency image | |
| CN116523904B (en) | Artificial intelligence-based metal stamping part surface scratch detection 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |